The Study Of Auto-adjusting Translation And Recommended Learning Platform On The Basis Of Knowledge Tree

The study and application of computer science in education dates back as early as 1950's. As the study theory, network technique, and artificial intelligence further develop, nowadays the intelligent study system based on network gradually improve the weakness of traditional ways of education, such as inefficient interaction, fewer ways of education, failure to accommodate to students'cognitive capacity, etc, all of which leads to the tremendous development for computer-aided education in the aspects of intellectualization and individualization.With the further deepening the technological and study knowledge of on-line education, and the increasing difficulties of organizing and sharing knowledge as a result of the diversity and intricacy of inter-disciplinary knowledge, not only should we materialize the cyberized curriculum, but also semanticize discipline resources. Only in this way the search of knowledge and acquisition can be made more effective and convenient. Therefore, the distance education platform built on semantic network overcomes the inability to understand the logics and significance of language, a flaw of traditional web. Given the fact that the current on-line study system is unable to automatically adjust to translate the same English language point into Chinese meaning in a specific field. This paper, relying on semantically-recommended language learning point, strives to purpose an assistant platform of distance study to address the above problems.The paper analyzes the features and requirements of the current based learning system, the ways of resource organization that supports study platform, namely knowledge map, ontology of semantic web, and the relevant technologies of semantic links net and puts forward an applied and efficient ways of resource organization, i.e. the subject knowledge tree build by semantic links net converted by knowledge map. In addition, the paper has also researched into the system structure of KTRP and the realization of such structure, as well as the principles and methods of building knowledge tree in this discipline. Based on the extensive research on the rules of engine governing the semantic inference for the knowledge point on this knowledge tree, and methods of individually-recommended knowledge point, and the principle and technology of similarity-identifying machine of knowledge points, the paper practised in building knowledge tree of graphics theory of database structure course of computer specialty and physics course and apply JPS and database technology to design and materialize the archetype system for learners that could provide them with a recommended knowledge point and automatically translate the Chinese meanings needed in this article field when they read English texts and looks for the knowledge points, especially multiply Chinese translations can be found in two different fields. Besides, a comparison and evaluation are also made between such new learning platform and past ones. At length, a conclusion is reached and discusses how to achieve module of learners and mutli-dimensional inference and dynamic knowledge points marking and recommended multi-dimensional language points and texts.