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本教材以项目任务驱动和活页式设计为核心，紧密围绕人工智能技术前沿，系统涵盖自然语言处理、语音与图像识别、机器学习、AIGC生成技术、具身智能及伦理治理等关键领域。项目内容跨专业应用融合，结合智能制造、医疗、金融等企业真实场景案例，依托定制化实验平台与低代码工具，配套微课、实操手册等数字化资源，支持理论与实践深度融合。教材通过"问题引导—探索实践—知识构建”流程，培养学生自主探究与解决多领域实际问题的能力，并融入人工智能伦理与社会责任思考，赋能高职学生成为智能时代所需的复合型技术技能人才。

第1部分  认识人工智能 ··················································································.1 
项目1  让机器“学会”学习——深度学习与智能引擎 ······························································.3 
任务1  寻找身边的智能应用 ·························································································.3 
任务2  回归模型 ········································································································.5 
任务3  分类模型 ········································································································11 
任务4  聚类分析 ······································································································.19 
任务5  交互式可视化人工神经网络 ··············································································.24 
项目2  让机器“理解”文字——自然语言处理 ·····································································.30 
任务1  短文本相似度分析 ··························································································.30 
任务2  新闻文本分类 ································································································.35 
任务3  新闻评论情感分析 ··························································································.40 
项目3  让机器“听懂”声音——语音识别与合成 ··································································.45 
任务1  语音信号处理 ································································································.45 
任务2  文本生成语音 ································································································.51 
任务3  实时语音翻译 ································································································.56 
项目4  让机器“看清”世界——计算机视觉 ········································································.61 
任务1  目标检测 ······································································································.61 
任务2  图像分类 ······································································································.67 
任务3  图像识别 ······································································································.77 
项目5  让机器“创作”内容——大语言模型与AIGC ·····························································.85 
任务1  提示词工程 ···································································································.85 
任务2  AI 图像生成 ··································································································.91 
任务3  AI视频理解 ··································································································.96 
任务4  AI视频生成 ·································································································.100 
第2部分  人工智能应用 ···············································································.107 
项目6  AI+智能办公 ·····································································································.109 
任务1  基于大模型快速生成思维导图 ··········································································.109 
任务2  办公自动化与 PPT 自动生成 ·············································································.117 
任务3  办公自动化与 Excel 表格处理 ···········································································.125 
任务4  办公自动化与 Excel 图表分析 ···········································································.135 
项目7  AI+教育人文 ·····································································································.145 
任务1  图文创作 ·····································································································.145 
任务2  试卷批改 ·····································································································.154 
任务3  构建成语填空练习助手 ···················································································.159 
任务4  创建教学教案生成器 ······················································································.167 
项目8  AI+医疗健康 ·····································································································.178 
任务1  基于机器学习的心脏病预测 ·············································································.178 
任务2  基于深度学习的癌细胞与正常细胞的自动鉴别 ·····················································.188 
任务3  构建医疗领域的知识图谱 ················································································.196 
任务4  基于本地知识库的中医药知识问答助手 ······························································.201 
第3部分  项目实践 ·····················································································.213 
项目9  中华美食制作助手 ······························································································.215 
任务1  初始化智能体与角色设定 ················································································.216 
任务2  打造智能体的核心能力——搭建菜谱生成工作流 ··················································.219 
任务3  集成图片生成与海报制作 ················································································.227 
任务4  整体测试与发布分享——优化体验并交付成果 ·····················································.234

本书是一本为高职高专院校量身打造的人工智能通识课教材。全书以“低代码开发·场景化实践·创新素质培养”
为核心理念，主要依托“海豚人工智能与大数据实验室”平台，帮助读者在不具备编程基础的情况下也能轻松完成相关
应用的开发，始终聚焦于理解人工智能的核心原理与创新应用。 
本书采用模块化的项目—任务结构，共三大部分9个项目，层层递进。第1部分涵盖深度学习、自然语言处理、语音
识别、计算机视觉及AIGC等关键技术；第2部分横向拓展至办公、教育、医疗等典型场景；第3部分通过完整的综合项
目进行实战。各项目之间相对独立，教师可以根据专业需求自由选择，组织灵活性强。

朱琼瑶，女，现任安康职业技术学院教师，长期从事计算机专业教学工作及管理工作，主要研究方向为人工智能与计算机。曾发表多篇学术论文，参与多项课题研究并取得相应成果。

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