Because the current diagnosis and treatment decision support system adopts a single subject decision-making method, resulting in low diagnosis and treatment accuracy and low accuracy of the obtained data classification results, a multi-disciplinary collaborative diagnosis and treatment decision support system based on improved K-Nearest Neighbor (K-NN) classification algorithm and Support Vector Mechine (SVM) is proposed and designed. Based on the overall framework of the system, the database system module, human-computer interaction module, and diagnosis and treatment reasoning module are designed. The diagnosis and treatment reasoning module is the software core of the system. The reasoning engine is established by improved K-NN classification algorithm and SVM. With the help of computer, medical cases similar to the patient’s disease information are searched, and similarity matching is carried out. According to the matching results, a new clinical case is constructed based on patient symptom set. The concept of Clinical Document Architecture (CDA) is introduced to realize the effective fusion of improved K-NN classification algorithm and SVM algorithm, and to complete the multi-disciplinary collaborative diagnosis and treatment decision. The experimental results show that, compared with the traditional system, the system has high accuracy in diagnosis and treatment decision-making, the average value of the evaluation index is 95.98%, and the accuracy rate of classification results is high. With the help of the system, it can improve the diagnosis accuracy of doctors and reduce the misdiagnosis rate, and the operation complexity is low.