计算机类技能型理实一体化新形态系列
人工智能通识教程
杨 鹏 余明辉 李秀秀 主 编钟闰禄 陈海山 谢海燕 副主编
清华大学出版社北 京
内容简介
本书紧扣职业教育“产教融合、实践导向”的政策要求，以人工智能“认知—技术—应用—伦理”为逻辑主线，采用项目化组织模式，从人工智能的演进脉络切入，逐步延伸至核心技术、典型场景应用与前沿伦理议题，带领读者走进人工智能的世界。
本书注重理论与实践相结合，知识点同步匹配案例讲解，既覆盖人工智能的基础概念、核心技术体系，也聚焦图像识别、语音处理、大模型、AIGC、具身智能等实用领域，每个项目都配套提供了清晰的思维导图与详细的实操步骤描述，兼顾通识知识传递与职业技能训练。全书以项目牵引、任务驱动为特色，既搭建起完整的人工智能知识框架，又强化场景应用、工具实操能力，帮助学习者在“做中学”过程中构建 AI 时代的核心素养。
本书可作为职业本科及高等院校人工智能通识类课程教材，也可作为人工智能爱好者的自学教材，同时可作为企事业单位开展数字化转型培训的参考资料。
本书封面贴有清华大学出版社防伪标签，无标签者不得销售。版权所有，侵权必究。举报：010-62782989，beiqinquan@tup.tsinghua.edu.cn。
图书在版编目（CIP）数据
人工智能通识教程 / 杨鹏，余明辉，李秀秀主编. 北京：清华大学出版社，2026. 6. --（计算机类技能型理实一体化新形态系列）. -- ISBN 978-7-302-71725-6
Ⅰ. TP18 中国国家版本馆CIP 数据核字第202618US10 号
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前 言
在数字经济蓬勃发展的今天，人工智能已成为赋能产业升级、推动职业教育改革的核心驱动力。2025 年9 月国务院印发的《关于深入实施“人工智能+”行动的意见》明确提出“把人工智能融入教育教学全要素、全过程”，教育部职业院校信息化教学指导委员会同步发布《职业院校人工智能应用指引》，明确职业本科人工智能教育需以产业需求为牵引、实践实训为支撑，聚焦“通识素养—专业技能—行业能力”三层递进培养，并划定“分层培育 AI 核心素养、强化场景化实践”的核心方向。本书立足这一系列政策要求，紧扣“产教融合、实践导向”本质，由校企合作共同编写，旨在搭建“认知—技术—应用—伦理”四位一体的人工智能知识体系，助力培养具备 AI 核心素养的复合型技术技能人才。
本书贯彻落实党的二十大精神，坚持立德树人，将知识传授、能力培养与价值引领有机融合。书中内容既呈现我国人工智能领军企业的创新成果、中华优秀传统文化的智慧传承，又强化人工智能伦理规范、技术向善与社会责任意识的培育。
本书的编写遵循“由浅入深、理实一体”的原则，以项目化教学为载体，系统覆盖人工智能的核心内容：从AI 发展脉络与基础概念切入，逐步深入机器学习、深度学习等核心技术原理；聚焦图像识别、语音处理、大模型、AIGC、具身智能等实用领域，结合工业质检、智能零售等职业场景拆解应用逻辑；最终延伸至 AI 伦理与社会影响的规范引导。全书以“案例解析+ 实操练习”的模式降低学习门槛，每个项目均配套思维导图和详细的实操步骤（含使用AIGC 进行视频创作、“校园迎新助手”智能体搭建等具体任务），让学习者在“做中学”中掌握实用技能。
作为人工智能通识课教材，本书具有三大特色：一是政策适配性，严格对标职业教育改革要求和“人工智能+”行动与素养标准；二是场景实用性，所有案例与实训任务均源自真实职业场景，强化技能迁移能力；三是资源丰富性，配套微课视频、源代码、教学课件、习题题库等一站式教学资源，方便教与学高效开展。
本书由杨鹏、余明辉、李秀秀担任主编，钟闰禄、陈海山、谢海燕担任副主编，曾青松、林颖、曾哲军、汤非易以及广东轩辕网络科技股份有限公司工程师黄永健、广东企想科技有限公司谢雄参与了本书的编写工作。广东轩辕网络科技股份有限公司为本书的编写提供技术支持，共同打造了人工智能通识课一体化实践平台，汇聚DeepSeek、即梦、扣子等国内主流 AI 开放平台，并提供配套实践案例操作流程。
在本书编写过程中，编者参考了诸多行业标准与前沿成果，在此向相关领域的专家学者表示衷心感谢。由于人工智能技术发展迅速，书中难免存在疏漏之处，恳请广大读者批评指正，共同推动职业教育人工智能教材的完善与发展。
编 者 2026 年2 月
目 录
项目1 探寻AI 的演进脉络：AI 历程回溯 ·············································· 1 项目背景 ·····························································································1 思维导图 ·····························································································2 项目相关知识·······················································································2
1.1 什么是人工智能 ·······································································2
1.2 人工智能从幻想走向科学 ···························································3
1.3 人工智能元年 ··········································································6
1.4 人工智能的潮起潮落 ·································································7
1.5 中国人工智能的发展历程 ·························································16 项目任务 ···························································································18 任务1-1 今昔对比看发展 ······························································18 任务1-2 以图搜图，让搜索更简单····················································19 项目小结与展望··················································································19 课后练习 ···························································································20
项目2 梳理AI 的“智慧图谱”：AI 核心技术 ··········································21 项目背景 ···························································································21 思维导图 ···························································································21 项目相关知识·····················································································22
2.1 相关概念 ··············································································22
2.1.1 人工智能的核心要素 ······················································22
2.1.2 人工智能的发展阶段 ······················································24
2.1.3 人工智能的两种形态 ······················································26
2.2 核心技术 ··············································································27
2.2.1 机器学习 ·····································································27
2.2.2 人工神经网络 ·······························································29
2.2.3 深度学习 ·····································································30
2.2.4 强化学习 ·····································································34
2.3 典型应用领域 ········································································35
2.3.1 计算机视觉 ··································································35
2.3.2 自然语言处理 ·······························································36
2.3.3 工业制造 ·····································································37
2.3.4 智能医疗 ·····································································38
2.3.5 金融科技 ·····································································39 项目任务 ···························································································41 任务2-1 观察与分析人工智能应用场景 ············································41 任务2-2 对比分析不同人工智能学习技术 ·········································42 任务2-3 研讨人工智能前沿应用案例 ···············································43 项目小结与展望··················································································45 课后练习 ···························································································45
项目3 解析计算机的视觉世界：图像识别技术应用································47 项目背景 ···························································································47 思维导图 ···························································································47 项目相关知识·····················································································48
3.1 图像识别技术基础 ··································································48
3.1.1 图像识别的基本概念 ······················································48
3.1.2 图像识别的基本流程 ······················································48
3.1.3 图像识别的技术指标 ······················································49
3.2 典型应用场景分析 ··································································49
3.2.1 人脸识别 ·····································································49
3.2.2 医疗影像诊断 ·······························································50
3.2.3 自动驾驶感知 ·······························································51
3.2.4 工业质量检测 ·······························································52
3.2.5 智能安防监控 ·······························································52
3.3 技术实现路径 ········································································53
3.3.1 传统图像识别技术 ·························································53
3.3.2 基于深度学习的图像识别技术 ··········································54
项目任务 ···························································································55 任务3-1 基于传统技术的图像识别实践 ············································55 任务3-2 基于深度学习技术的图像识别实践 ······································59 任务3-3 实际应用场景模拟 ···························································62 项目小结与展望··················································································71 课后练习 ···························································································72
项目4 解码声音的辨识密钥：语音识别技术应用···································73 项目背景 ···························································································73 思维导图 ···························································································74 项目相关知识·····················································································74
4.1 语音信号处理基础 ··································································74
4.1.1 声音的物理特性与数字化 ················································74
4.1.2 特征提取方法 ·······························································76
4.1.3 噪声处理技术 ·······························································78
4.2 传统语音识别模型 ··································································80
4.2.1 声学模型 ·····································································80
4.2.2 语言模型与解码器 ·························································81
4.3 端到端语音识别模型 ·······························································82
4.4 语音合成技术 ········································································84
4.4.1 TTS 流程概述 ·······························································84
4.4.2 传统合成方式概述 ·························································85
4.4.3 神经网络合成方法 ·························································86
4.5 技术应用场景 ········································································87 项目任务 ···························································································90 任务4-1 语音识别系统构建 ···························································90 任务4-2 文本转语音系统设计 ························································93 任务4-3 语音交互系统设计 ···························································95 项目小结与展望··················································································99 课后练习 ··························································································100
项目5 解构大模型的生成密码：大模型技术·······································102
项目背景 ··························································································102
思维导图 ··························································································102 项目相关知识····················································································103
5.1
大模型基础 ··········································································103
5.1.1
核心任务类型 ······························································103
5.1.2
语义表示与语义嵌入 ·····················································105
5.2
大模型核心架构 ····································································106
5.2.1
注意力机制 ·································································106
5.2.2
Transformer 原理 ··························································108
5.2.3
典型架构与模型分类 ·····················································109
5.3
模型训练与优化方法 ······························································111
5.3.1
预训练与全量参数微调 ··················································111
5.3.2
高效参数微调 ······························································113
5.3.3
提示词设计 ·································································114
5.4
典型应用场景 ·······································································116
5.4.1
智能问答与教学助理 ·····················································116
5.4.2
文本生成与内容创作 ·····················································117
5.4.3
智能翻译与语言理解 ·····················································117
5.5
技术前沿与发展趋势 ······························································118
5.5.1
多模态智能模型 ···························································118
5.5.2
专用领域模型 ······························································119
5.5.3
模型可解释性与安全合规 ···············································119
项目任务 ··························································································121 任务5-1 绘制“校园教务问答大模型工作原理”简图 ····························121 任务5-2 提示工程与提示框架 ·······················································123 任务5-3 体验并比较国内3 个大模型平台 ········································127
项目小结与展望·················································································129 课后练习 ··························································································130
项目6 探索AI 的生成艺术：AIGC 应用 ············································132 项目背景 ··························································································132 思维导图 ··························································································132 项目相关知识····················································································133
6.1 认识AIGC ···········································································133
6.1.1
什么是AIGC ·······························································133
6.1.2
AIGC 的发展历程 ·························································133
6.1.3
AIGC 的生成原理 ·························································133
6.1.4
AIGC 的行业应用 ·························································134
6.1.5
AIGC 的使用方法 ·························································136
6.2
AIGC 在文案创作中的应用 ······················································138
6.2.1
文案创作提示词优化技巧 ···············································138
6.2.2
不同场景下的AIGC 文案创作策略 ···································138
6.2.3
AIGC 文案创作的合规性与伦理边界 ·································142
6.3
AIGC 在图像设计中的应用 ······················································142
6.3.1
图像生成关键技术 ························································142
6.3.2
常见的AI 图像生成工具 ················································144
6.3.3
AI 画图提示词设计 ·······················································145
6.4
AIGC 在视频创作中的应用 ······················································146
6.4.1
视频生成的常用工具 ·····················································146
6.4.2
视频生成的提示词设计 ··················································148
6.4.3
视频生成的步骤 ···························································149
6.5
AIGC 在程序设计中的应用 ······················································149
6.5.1
AI 在程序设计中的应用 ·················································150
6.5.2
常见AI 辅助程序设计工具 ·············································150
6.5.3
AI 辅助程序设计提示词技巧 ···········································151
项目任务 ··························································································152 任务6-1 使用AIGC 设计“秋分”节气海报 ········································152 任务6-2 使用AIGC 创作文创产品“醒狮”宣传视频 ····························155 任务6-3 使用AIGC 生成“课堂点名积分”系统 ··································161
项目小结与展望·················································································163 课后练习 ··························································································163
项目7 构建AI 的场景赋能：智能体应用···········································165 项目背景 ··························································································165 思维导图 ··························································································165 项目相关知识····················································································166
7.1 什么是AI 智能体···································································166
7.1.1
传统视角下的智能体 ·····················································166
7.1.2
大模型驱动的新范式 ·····················································167
7.2
智能体的核心组成 ·································································169
7.3
智能体开发平台介绍 ······························································172
7.4
智能体开发平台特性 ······························································173
项目任务 ··························································································174 任务7-1 高级翻译助手 ································································174 任务7-2 大学校园迎新助手 ··························································179 任务7-3 古诗词情景再现 ·····························································184
项目小结与展望·················································································202 课后练习 ··························································································203
项目8 激活AI 的身体语言：具身智能··············································204 项目背景 ··························································································204 思维导图 ··························································································204 项目相关知识····················································································205
8.1
具身智能体的定义与特点 ························································205
8.1.1
具身智能体的起源与发展 ···············································205
8.1.2
具身智能体的基本定义 ··················································209
8.1.3
具身智能体的主要特点 ··················································211
8.2
具身智能的感知、决策与执行 ···················································212
8.2.1
感知 ··········································································213
8.2.2
决策 ··········································································215
8.2.3
执行 ··········································································217
8.3
具身智能基础应用场景 ···························································218
8.3.
智能制造 ····································································219
8.3.
服务机器人 ·································································219
8.3.
自动驾驶 ····································································219
8.3.
医疗康复 ····································································220
8.3.
农业自动化 ·································································220
8.3.
教育与科研 ·································································220
8.4
Gazebo 仿真环境简介 ·····························································221
8.4.1 Gazebo 的界面·····························································221 8.4.2 Gazebo 能做什么··························································221
项目任务 ··························································································223 任务8-1 Gazebo 中简单小车模型的构建 ··········································223 任务8-2 Gazabo 小车移动控制 ······················································227
项目小结与展望·················································································229 课后练习 ··························································································230
项目9 理解AI 的价值坐标：AI 伦理与社会 ········································232 项目背景 ··························································································232 思维导图 ··························································································233 项目相关知识····················································································233
9.1
AI 伦理的起源与发展 ·····························································233
9.1.1
从“工具理性”到“价值理性”············································233
9.1.2
从“算法无害”到“AI 有责”··············································234
9.1.3
国际 AI 伦理治理简史····················································235
9.1.4
中国 AI 伦理政策与实践进展···········································236
9.2
AI 伦理的典型案例与冲突本质 ·················································237
9.2.1
AI 伦理的典型案例 ·······················································237
9.2.2
AI 伦理冲突 ································································238
9.3
AI 伦理的治理基石：原则与责任 ···············································240
9.3.1
人类优先：守住“以人为本”的红线····································240
9.3.2
公平可及：防止“算法歧视”和“数据贫困”···························241
9.3.3
透明可解释：让黑箱变“明箱”··········································242
9.3.4
责任可追溯：构建“可问责”的AI 系统 ·······························244
9.4
AI 伦理的多元治理路径 ··························································245
9.4.1
技术端：让AI“可解释、可约束、可终止”···························245
9.4.2
行业端：行业自治与伦理评估机制····································246
9.4.3
社会端：公众教育与多方监管··········································248
9.5
AI 浪潮下的就业重塑与应对 ····················································248
9.5.1
AI 对职业结构的冲击 ····················································248
9.5.2
新兴职业与技能转型 ·····················································249
9.5.3 当代学生的生存智慧 ·····················································250 项目任务 ··························································································251
任务9-1 AI 伦理典型案例深度剖析 ················································251 任务9-2 AI 伦理角色扮演辩论会 ···················································252 任务9-3 AI 伦理评估模拟实训 ······················································253 项目小结与展望·················································································253 课后练习 ··························································································254
参考文献······················································································256
