In this paper, based on the characteristics of real-time and time characteristics of financial data, time series is used as an entry point to mine financial data. Using the trigonometric polynomial graph method, the visualization of financial and economic cube data and the definition of clustering distance are carried out. Combined with wavelet analysis theory to decompose the periodic, trend and random terms of financial data time series, so as to predict the development of financial data with different scale components. In addition, BIRCH method is utilized to construct CF tree through data clustering features for micro-clustering of financial data. And K-meadiods method is chosen to fill the gap of BIRCH method in the application of large datasets to realize the macro-clustering of financial and economic data, and to establish BIRCH.K-meadiods clustering method. Compared with similar models, the prediction and clustering model of financial data based on time series has an overlap rate of >99.00% between the predicted and actual values of financial data and an error in the interval of (0,300), which can assist in the in-depth mining and clustering analysis of the value of financial data with high-precision prediction, and expand the level of decision-making considerations for the internal management of the enterprise and even for the external construction.
