Merge pull request #221 from Learnware-LAMDA/polish_contents

[DOC] polish contents

README.md

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 # Introduction
 
-The _learnware_ paradigm, proposed by Professor Zhi-Hua Zhou in 2016 [1, 2], aims to build a vast model platform system, i.e., a _learnware dock system_, which systematically accommodates and organizes models shared by machine learning developers worldwide, and can efficiently identify and assemble existing helpful model(s) to solve future tasks in a unified way.
+The _learnware_ paradigm was proposed by Professor Zhi-Hua Zhou in 2016 [1, 2]. In this paradigm, developers worldwide can share models with the _learnware dock system_, which effectively searches for and reuse learnware(s) to help users solve machine learning tasks efficiently without starting from scratch.
 
 The `learnware` package provides a fundamental implementation of the central concepts and procedures within the learnware paradigm. Its well-structured design ensures high scalability and facilitates the seamless integration of additional features and techniques in the future.
 

README_zh.md

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 # 简介
 
-学件范式由周志华教授在2016年提出 [1, 2]，旨在构建一个巨大的模型平台系统：即学件基座系统，系统地组织管理世界各地的机器学习开发者分享的模型，并通过统一的方式识别、利用已有模型的能力快速解决新的机器学习任务。
+学件范式由周志华教授在2016年提出 [1, 2]。在学件范式下，世界各地的开发者可分享模型至学件基座系统，系统通过有效查搜和复用学件帮助用户高效解决机器学习任务，而无需从零开始构建机器学习模型。
 
 本项目开发的 `learnware` 包对学件范式中的核心组件和算法进行了实现，全流程地支持学件上传、检测、组织、查搜、部署和复用等功能。基于良好的结构设计，`learnware` 包具有高度可扩展性，为后续相关算法和功能的开发打下坚实基础。
 

docs/start/intro.rst

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 Introduction
 ================
 
-The *learnware* paradigm, proposed by Professor Zhi-Hua Zhou in 2016 [1, 2], aims to build a vast model platform system, i.e., a *learnware dock system*, which systematically accommodates and organizes models shared by machine learning developers worldwide, and can efficiently identify and assemble existing helpful model(s) to solve future tasks in a unified way.
+The *learnware* paradigm was proposed by Professor Zhi-Hua Zhou in 2016 [1, 2]. In this paradigm, developers worldwide can share models with the *learnware dock system*, which effectively searches for and reuse learnware(s) to help users solve machine learning tasks efficiently without starting from scratch.
 
 The ``learnware`` package provides a fundamental implementation of the central concepts and procedures within the learnware paradigm. Its well-structured design ensures high scalability and facilitates the seamless integration of additional features and techniques in the future.