The medical record system is becoming more and more irreplaceable in the medical industry, and the electronic medical record data continues to grow over time. There is a lot of knowledge and information in these accumulated medical resources that can be used for medical services, but how to obtain this valuable medical information is a difficult problem that needs to be overcome. Event extraction belongs to information extraction technology, which is an effective solution that can automatically mine knowledge and information from text data. Many studies have applied it to the text data of electronic medical records to extract medical events related to medical treatment. They have achieved certain results for medical services. However, these studies usually lack the synergistic consideration of global features and local features of medical text information in terms of Chinese medical record text mining and utilization. To better solve this problem, we try to propose a hybrid neural network model (BCBC) based on CNN-BILSTM-CRF. By integrating CNN and BILSTM, the local and global features of the text are comprehensively extracted, which makes up for the insufficient semantic capture of a single model in the traditional method. Through experimental verification, the hybrid neural network model BCBC proposed in this paper outperforms other previous advanced methods in event extraction and can efficiently complete the event extraction task.

Analyzing character sentiment information through audio is an essential and challenging task. Using convolutional and recurrent neural network approaches for audio sentiment analysis has achieved initial results, proving that deep learning methods can be helpful for sentiment analysis of audio. With the rise of multimodal research, using audio and text feature fusion for sentiment analysis has better results. However, this approach uses the pipeline approach for analysis, which needs to use an additional speech-to-text model to get text information first so that the error of text information conversion will affect the subsequent judgment. In order to solve this problem, we propose a new end-to-end model, which adopts the model architecture of seq2seq, encodes audio information by the encoder, generates text information of audio by the decoder and understands the textual content, and finally obtains the final emotional state by fusing the features extracted by the decoder and the encoder. Our model has experimented on actual psychological assistance hotline data, and the results show that our method is significantly better than the baseline method, which is a meaningful method.