In today’s rapidly developing financial technology, traditional financial education models have encountered problems such as inefficiency and unsuitability. This chapter intends to conduct research on the design and optimization of a financial course system based on sacked denoising autoencoder (SDAE). This chapter intends to use an improved SDAE algorithm to extract deep features from financial data, in order to achieve deep mining of financial data and effectively enhance the system’s data processing ability and learning efficiency. The SDAE network optimizes the financial curriculum system with its deep feature extraction, ability to cope with complex nonlinearities, personalized teaching, ability to improve efficiency and quality, and ability to adapt to industry development, ensuring that education keeps up with the forefront of finance and cultivating high-quality financial talents. By integrating this network with educational models, a learning process-based teaching feedback mechanism was constructed to dynamically adjust teaching content and methods, ensuring the personalization and optimization of teaching activities. It was compared with traditional teaching methods. The results indicated that this system had good teaching effectiveness and could effectively improve students’ learning enthusiasm. The satisfaction score ranged from 4.3 to 4.9, and the high satisfaction of most students with the teaching process indicated that personalized teaching strategies had a significant effect on meeting students’ needs. The research findings of this chapter provided a new method for financial education, which can not only improve students’ financial analysis and decision-making skills but also help them to have a deeper understanding of complex financial problems and promote the development of financial education.