The design of teaching resources optimization algorithm is critical in generator phase advance limit, however it has an issue with erroneous performance positioning. The typical Support Vector Machine SVM is unable to address the the phase limit issue in generator phase advance limit, and the result is insufficient. As a result, a Big data analysis-based optimization algorithm design of information teaching resources is provided, and the optimization algorithm design of information teaching resources is assessed. To begin, the DS theory is used to discover the influencing elements, and the indicators are split based on the design of teaching resources optimization algorithm’s needs to decrease interference factors in the design of teaching resources optimization algorithm. The DS theory is then used to create a Big data analysis design of teaching resources optimization algorithm scheme, and the outcomes of the design of teaching resources optimization algorithm are thoroughly examined. The MATLAB simulation results reveal that, under particular evaluation conditions, the Big data analysis outperforms the standard Support Vector Machine SVM in terms of design of teaching resources optimization algorithm accuracy and time of influencing variables.