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The main complaint information of patients in the medical field is the key to medical text classification work, which can provide strong support for smart healthcare and information text classification. In recent years, with the development and application of deep learning, there have been numerous quality control models for the entire process of medical records based on traditional deep learning techniques. However, traditional models have many shortcomings and limitations, such as slow training speed, accuracy loss, overfitting, and inability to handle large-scale data. Therefore, improved deep learning algorithms have been introduced. The experiment result of the whole process medical record connotation quality control system based on in-depth learning under the guidance of the guide is that when the word vector is set to 160, the Bidirectional recurrent neural networks (BiGRU-SA) model has the best effect, with an accuracy rate of 84.9%. The accuracy of BiGRU-SA Model is not significantly affected by the vector dimension. However, the accuracy of the improved transformation extraction evolutionary CNN (TextCNN) model increases exponentially when it is updated in the third and fourth iterations, and reaches the ideal value of 8.3 in the third iteration × 10-1. As the number of iterations increases, the accuracy of the model shows a trend of first increasing and then decreasing. In the sixth iteration, the model performs best with an accuracy of 84.9%. The optimized whole process medical record connotation quality control model has improved the area value, accuracy, F1, and recall rate under the rate of change index. The above results can better solve the problems of overfitting and feature information loss, and achieve the control of the connotation quality of the entire process medical record.