The end-to-end Transformer model based on the self-attention mechanism shows superior performance in speech recognition. However, this model has limitations in capturing local feature information during shallow processing and does not fully consider the interdependence between different blocks. To address these issues, this study proposes Conformer-SE, an improved end-to-end model for speech recognition. The model first adopts the Conformer structure to replace the encoder in the Transformer model, thus enhancing its ability to extract local features. Next, by introducing the SE channel attention mechanism, it integrates the output of each block into the final output through a weighted sum. The experimental results on the Aishell-1 dataset show that the Conformer-SE model reduces the character error rate by 18.18% compared to the original Transformer model.