This study proposed an intervention model for improving the teaching ability of teachers in general practice standardized training bases through intelligent computing and decision support. Focusing on the scenes of teaching rounds, case discussion, outpatient guidance and skill teaching, a data set containing 126 teachers from 8 general practice standardized training bases, 18,640 behavior records, 4,320 trainees ‘feedback and 1,260 structured evaluation forms was constructed. The teaching frequency, interaction intensity, supervision stability and feedback consistency are encoded by the timing feature representation module, and the intervention suggestions for different ability shortboards are generated by the adaptive scheduling algorithm. A system consisting of data access layer, capability analysis layer, decision service layer and interactive application layer was developed to support data maintenance, identification, intervention allocation and result tracking. Experimental results show that the accuracy of this method is 93.1%, the F1 value is 91.8%, and the average response time is 1.4 s. It has good application and deployment value in complex clinical teaching scenarios.
