Bug reports play a crucial role in software development, but the growing volume of reports presents challenges such as duplicate bug reports and information overload. This paper proposes SURec, a method for recommending similar bug reports that integrates semantic analysis with user behavior patterns to improve recommendation accuracy and personalization. SURec extracts semantic features (summaries and descriptions), categorical attributes (products and components), and user behavior features to achieve personalized similarity calculation. Four user information datasets were constructed using Bugzilla (Eclipse, Mozilla) and JIRA (Hadoop, Spark) platforms. Experimental results demonstrate that on large-scale datasets, our method implemented with pre-trained language models achieves higher recommendation accuracy in complex semantic scenarios. In contrast on small-scale datasets with sparse data and noisy categorical attributes, statistical models such as TF-IDF and LSI demonstrate greater robustness. The findings confirm the effectiveness of integrating user historical behavior with multi-dimensional attributes for similar bug report recommendations.