Abstract:SVDD classification algorithm based on SVM has defects, such as high calculation complexity property and low accuracy. According to nonlinear and high-noise characteristics of stock data, inspired from the idea of traditional SVDD classification algorithm, the proposed algorithm (FCABFKH) adopts mergence method to find hypersphere sets and maximum membership degree law to construct classifier. By this means, the algorithm can rule out off-group points and hypersphere sets overlap problem. Furthermore, it can avoid complex quadratic programming. Consequently, FCABFKH provides faster rate and higer accuracy. Using the data of listed companies of China A stocks market, experiments are done to test the validity of the method mentioned above. The result indicates that portfolio's return rate using classification method of FCABFKH is higher than the market benchmark.