In the age of the big data and artificial intelligence, Support Vector Machine (SVM) has been successfully applied in many aspects and becomes one of the common methods to solve classification problems. But the real world data is usually imbalanced, making its performance of classification significantly decreased. This study proposes to improve original standard Fuzzy Support Vector Machine (FSVM) by using inequality hyper-plane distance. The algorithm introduces parameter λ to controls the distance between hyper-plane and categories, and constructs fuzzy membership function by calculating sample mutually center distance, which can improve the falling precision of classification caused by imbalanced distribution of sample and noise data. The effectiveness of the proposed algorithm is verified by experiments, and the result shows that the proposed algorithm has a better effect of imbalanced data.