Design of a Visual Training System for Software Engineering Education Based on Knowledge Graphs

Abstract

With the increase of the content and difficulty of software engineering education courses, software engineering education visual training system came into being, but the technology is not mature at present, and the representation learning algorithm part of the visual training system needs to be optimized. In order to solve this problem, the research proposes to optimize the take model by using the trans representation algorithm, and embed the optimized knowledge map into the new software engineering education visual training system. The performance of TEKEE model is verified by comparing TEKEE model with Trans E model, Trans D model and TEKED model. The experimental results show that the MR value of the optimized TEKEE model is 62, Hits@10 The value is 0.92, which is better than the other three representation learning models. In terms of the bearing capacity test of the visual training system, the response time of the business operation of the research and design training system is 1.22 seconds, and the CPU occupancy of the application server is 12.5%, all of which are normal. The experimental results show that the performance of the optimized TEKEE model has indeed been greatly improved, and the visual training system composed of the optimized knowledge map has a very good carrying capacity, which can provide a new idea for the software engineering education and training system.