Analyzing the causes of criminal psychology is crucial for assessing criminal risks and develop psychological correction plans. Traditional models have limitations in comprehending criminal psychology texts， revealing correlations between prompt vectors and downstream tasks， and identifying the importance of key terms dynamically. Therefore， a CP-BERT （Criminal Psychology-Bidirectional Encoder Representations from Transformers）-based attribution classification model for criminal psychology was proposed， enabling four-class classification of psychological causes of crime. First， an 8.03-million-character criminal psychology corpus was constructed for domain-adaptive pre-training， and semantic modeling capability of the model was enhanced. Second， a hybrid prompt learning method was employed to divide prompt templates into prefix and suffix segments： highly relevant prior information was injected into the prefix segment during the initialization phase， while random initialization was performed on the suffix segment by using normal distribution to supplement information uncovered by the prefix segment， and optimal proportions were determined experimentally. Third， an improved TF-Chi （Term Frequency-Chi-Square） association probability feature method was proposed， in which numerical features were mapped to information relevance probability values dynamically， and feature fusion was achieved by constructing a weight matrix. Finally， an annotated dataset consisting of 3 484 personal and criminal histories was constructed for model training and evaluation. Experimental results demonstrate that this model has an accuracy of 91.25% and an F1 score of 91.56%， significantly outperforming multiple baseline models such as ChatGLM and DeepSeek in all metrics， verifying its effectiveness in criminal psychological attribution tasks.