Software requirement analysis and intelligent recommendation method combined with knowledge map

With the increasing complexity of software system, it is a key challenge to improve the accuracy of software requirements analysis and the effectiveness of intelligent recommendation. This paper focuses on exploring innovative methods of software requirements analysis and intelligent recommendation combined with knowledge map. By collecting multi-source information such as software requirements documents, user behavior data and feedback, the software requirements knowledge map is carefully constructed. On this basis, the software requirements are analyzed by graph mining technology and reasoning mechanism, and intelligent recommendation is realized by graph-based random walk algorithm with context information. In order to verify the effectiveness of the method, experiments were carried out with 100 enterprise management software users as samples. The results show that the accuracy of demand analysis in the experimental group is 85%, which is significantly improved compared with the control group's 68%. In terms of intelligent recommendation, the recall rate and accuracy rate of the experimental group were 86.7% and 87.5%, while those of the control group were only 61.1% and 64.3%. This shows that the method of combining knowledge map has achieved remarkable results in mining potential software requirements, understanding user requirements and providing accurate recommendations. It provides valuable new ideas for related research and practice in the field of software development, and is expected to promote the further innovation and development of technology in this field.