Proposed a partial binary tree twin support vector machine multi-classification algorithm based on optimal classification features （OCF-PBT-TWSVM） to achieve effective classification of non-stationary transient random signals with edge distortion of tooth profile images， and to meet the requirements of real-time gear vision measurement and distortion compensation accuracy Claim. Selected the maximum value ${\mathit{v}}_m$ of the edge dynamic component signal， the position of the edge distortion signal q_u， and the edge distortion rate r_lv to formed the feature vector，which constituted the training sample set and the test sample set at the same time； defined the variable weight feature vector measure $\gamma$ with the target of distortion compensation， and completed the construction of the OCF-PBT-TWSVM algorithm according to $\gamma$ decreasing； used the particle swarm optimization method to optimize the algorithm parameters to optimize the performance of the $c_{\mathrm{1}}$， $c_{\mathrm{2}}$， and $g$ parameters. The test results show that， the final classification accuracy of the OCF-PBT-TWSVM multi-classification algorithm proposed in this paper is 96.96% in the case of small sample data， which has better classification effect and training speed than the PBT-SVM multi-classification algorithm. It is faster and can satisfy the requirements of subsequent distortion compensation measurement accuracy and real-time gear vision measurement.