A duplicate bug report detection model with enhanced text relevance semantics and multi-feature extraction was proposed to address the issues of semantic long-distance dependence and the singleness of bug report features in the current research on duplicate bug report detection. The model introduced the self-attention mechanism to capture the semantic relevance within the bug report text sequence. This mechanism calculates the contextual semantic vector dynamically for semantic analysis and resolves the problem of long-distance dependence. Additionally, the model employed the latent Dirichlet allocation algorithm to capture the topic characteristics of the bug report text. Furthermore, a feature extraction network was constructed to calculate category difference features, providing category information for the bug report simultaneously. Finally, comprehensive detection was performed based on three types of feature vectors. The experimental results demonstrate that the model achieves improved detection performance.