In actual pattern classification tasks, the processing data is generally imbalanced. Traditional pattern classification models tend to learn towards the majority class and ignore the minority class samples, leading to classifier performance deterioration. This paper proposes an adaptive synthetic sampling method for the imbalanced data processing using a variation Bayesian optimized GMM(VBoGMM) estimation method. The VBoGMM can automatically attenuate to the real number of Gaussian components to achieve the optimal estimation of any distribution. Based on the spatial distribution characteristics of unbalanced data sets, the adaptive synthetic sampling is performed, and the Tomek-link approach is further adopted to clean over-sampling samples to obtain a relatively balanced data set for the subsequent classifier learning. A large number of comparative experiments have been carried out on multiple public imbalanced data sets. Experimental results show that the proposed method can achieve relatively balanced samples while maintaining their spatial distribution characteristics of majority and minority samples, thus effectively improving the performance of traditional classifiers on various uneven data sets.