Architecture and Computational Optimization of Community Employment Support System for Digitally Intelligent Students Led by Digital Party Building Based on Field Theory

In this paper, K-prototype algorithm is chosen to cluster and analyze the data of students' behavior in the educational field.Further, a model of students' employment interest is constructed based on the job rating data of different classes of students.The timeliness is introduced in the model to improve the recommendation accuracy.Synthesize the algorithm and model to build an employment support system.Apply the system to the clustering study of college students' behavioral data to verify its career recommendation value.Set up comparison experiments to find the optimal similarity fitting parameters and number of neighbors to improve the system recommendation accuracy and judge the system recommendation effect.Preliminarily divide students into 3 categories by analyzing students' online behavior and book borrowing behavior.Preliminarily categorize students into 4 categories based on their grades.Combined with the performance labels and grade categories of professional courses, the employment direction of students was finally clustered into four categories, namely "postgraduate entrance examination", "civil servant application", "company work" and "others".The highest accuracy of the system job recommendation is achieved when the similarity fitting parameter = 0.5 and the number of neighbors N = 50.The RMSE value of the K-prototype algorithm ranges from 0.6011 to 0.731, and the recommendation effect is better than the comparison algorithm.