In the field of medicine, there are differences between patients with the same disease, and seemingly simple diseases may show different levels of complexity, which brings great challenges to patient identification, treatment, and prognosis. In this study, the electronic medical history stored in vertically unstructured time sequence is used to solve the heterogeneity of patients, enhance the acquisition of hidden information by seizing the characteristics of irregular medical treatment intervals, and capture the connection between current medical records and past and future information through forward and backward bidirectional learning, so as to deepen the level of feature extraction of original sequences and make the model make more accurate decisions. The BT-DST model proposed in this study?uses a time-aware LSTM unit to construct a bidirectional autoencoder to learn a strong single representation of a patient, which is then used in patient clustering to obtain the subtype of the patient for the current disease through statistical analysis. In addition, different types of therapeutic interventions can be applied to different populations, which provides precise medicine for different types of patients according to their health conditions.