In the traditional Fuzzy C-Means (FCM) clustering algorithm, the initial clustering center is uncertain and the number of clusters should be preset in advance which may lead to inaccurate results. The fuzzy clustering algorithm based on midpoint density function was put forward. Firstly, the stepwise regression thought was integrated as the initial clustering center selection method to avoid convergence from local circulation, and then the number of clusters was determined, finally according to the results, the validity index of fuzzy clustering including overlap degree and resolution was judged to determin the optimal number of clusters. The results prove that, compared with the traditional improved FCM, the proposed algorithm reduces the number of iterations and increases the average accuracy by 12%. The experimental results show that the proposed algorithm can reduce the processing time of clustering, and it is better than the comparison algorithm on the average accuracy and the clustering performance index.