Research on Digital Portrait Matching System for Accurate Employment of Graduates by Integrating Professional Information

User profile is an important means for enterprises to understand and obtain their potential customers. However, the existing statistical analysis algorithms cannot effectively deal with unstructured text information, nor can they deeply dig out the characteristics of users from many aspects. Aiming at the personalized employment needs of college students, this project intends to carry out the research on employer characteristic modeling based on multi-source and heterogeneous job-hunting big data for college students' personalized job-hunting needs. The research and application of evaluation index and labeling system, multi-source data fusion method, evaluation labeling method are mainly carried out. This project intends to use LDA technology to mine network content and build topic modeling through cross-domain collection and classification of book resources. According to user characteristics and content, a service oriented personalized recommendation algorithm is designed. This project proposes a group behavior detection algorithm, which can effectively reduce the group query time and computation amount by classifying the group characteristics. In this way, the purpose of fast and efficient. Simulation results show that the algorithm is effective.