Research on Evaluation Method of Medical Rehabilitation Teaching Quality Based on Historical Big Data Decision Tree Classification

The current evaluation matrix of medical rehabilitation teaching quality is mostly one-way, and the scope of evaluation is limited, resulting in an increase in the average difference of evaluation. Therefore, the design and research of the evaluation method of medical rehabilitation teaching quality based on historical big data decision tree classification is proposed. According to the actual measurement and analysis, set the basic teaching quality evaluation indicators, use the multi-level form, break the limitation of the evaluation range, develop the multi-level evaluation matrix, design the decision tree classification evaluation structure, build the historical big data decision tree classification evaluation model, and use the top-level improvement analysis to achieve quality evaluation. The final test results show that the analysis of the test results has been completed: after five cycles of measurement, the quality of medical rehabilitation teaching has been evaluated for five items, namely, professional ethics, teaching ability, teaching methods, teaching arrangements, and teaching effects. The final evaluation mean difference has been well controlled below 1.5, indicating that this evaluation method is highly targeted and stable, it has practical application value.