Despite data analysis being vital in vocational school classroom management, issues arise due to improper evaluation and placement. Traditional bee colony algorithms fail to address teaching management challenges in vocational colleges, yielding suboptimal outcomes. Therefore, this article introduces and explores a decision tree algorithm-based approach to higher vocational education teaching management data analysis. It also scrutinizes the data analytics of teaching administration in these institutions. Firstly, gradient descent theory aids in pinpointing influential factors, and indicators are segmented according to data analytical requirements to minimize disruptive elements in the process. Subsequently, employing the gradient descent concept, a decision tree algorithm for data analytics is devised, and its findings are meticulously evaluated. When gauged against specific criteria, MATLAB simulation results reveal that the decision tree algorithm surpasses conventional bee colony techniques in data analytics precision and processing speed.