The first part of this paper investigates the implementation process of the Adaboost regression algorithm, initializes the training set weights to obtain the weak learner, and builds the final strong learner. Then, we explore the treatment of corporate financial management concept drift by assigning variance weights to financial data samples according to their temporal proximity to solve the problem of financial concept drift. Then, an optimized AdaBoost-SVM model is constructed to initialize the sample weights according to time and sample category, calculate the classification error, and build a weak classifier and a decision classifier based on the samples. Finally, the company's financial management costs, personnel, and model prediction accuracy were analyzed. The model predicts financial distress with a 78% accuracy, financial insolvency with a 93% accuracy, and financial normalcy with a 90% accuracy. This confirms the feasibility of this paper's methodology for corporate financial management.