Financial data analysis is an important step in the investment decision-making process, and the application of deep learning methods in the field of financial data analysis has achieved good results. However, there are still some challenges to be solved in the application process. In this paper, the improved SDAE network algorithm is utilized for the construction of financial data analysis model, and experiments are conducted and so on. The algorithm combines autoencoder and denoising methods to learn more useful feature representations through a multi-layer stacking approach, and is therefore more discriminative. The structure of the modeling process and parameter optimization were improved in order to make the financial data analysis model and the algorithmic model more computationally efficient. Experimental results show that the accuracy of this paper’s method in financial analysis is up to 99%, and the financial data analysis model based on its improved SDAE network achieves good performance in financial data prediction tasks.