[Objective] The paper mainly studies the feature extraction of dynamic semantic information in the Chinese task entity relationship and the Chinese character relationship recognition. [Methods] In this paper, the public corpus of character entity relationship is used, and the attention mechanism + improved convolution neural network model is used to automatically extract features from the training data. The experimental results are compared and verified from the multi-dimensional aspects of entity relationship recognition efficiency of different models, entity relationship extraction effect of different relationship labels and entity relationship extraction efficiency of different vector training sets. [Results] Experimental results show that CNN+Attention model is superior to SVM, LR, LSTM, BiLSTM and CNN model in the prediction accuracy and global performance of Chinese character relationship extraction task. And it is 0.9% higher in accuracy, 0.8% higher in recall and 0.8% higher in F1 value than BiLSTM model with relatively better extraction effect. [Limitations] Only a single sample data source is used, multiple data source channels have not been expanded, and the sample data set is not wide enough. [Conclusions] The convolutional neural network based on the attention mechanism can effectively improve the accuracy and recall rate of entity relationship extraction in the task of Chinese character relationship extraction.