Semantic Role Labeling (SRL) serves as the foundational and pivotal technology for semantic analysis. However, methods relying on pre-trained language models are constrained by issues such as semantic ambiguity and training complexity. Addressing this concern, this paper proposes a Chinese SRL approach that integrates pre-trained language models with Biaffine technology, aiming to enhance the model’s capability in processing semantic information from long sentences while reducing training complexity. By incorporating pooling techniques and part-of-speech features, the model exhibits significant improvements in capturing semantic role boundary relationships. Experimental results demonstrate that the RoBERTa-MPBF model employing maximum pooling achieves an F1 score of 90.89% on the CPB dataset, outperforming models solely based on conditional random fields. Moreover, the introduction of part-of-speech tagging results in an average F1 score improvement of approximately 1.5%. Despite the increased computational burden, considering the performance enhancement, this additional time cost is deemed acceptable. In convolutional kernel size testing, the model maintains F1 scores between 88.6% and 88.8% when the kernel size is 2 or 3. However, as the kernel size increases to 4, the F1 score drops to 80.37%, and further increases to 5 result in an F1 score reduction to 69.51%.