The invention discloses a neural network model optimization method based on class expansion learning. The method specifically comprises the following steps: obtaining a data set which is used for training and contains various types of samples, and defining an algorithm target; extracting features of each type of pictures in the data set by using a universal model, and evaluating the error-prone degree of each type according to the distribution condition of each type of features; adding several types of data with the highest error-prone degree into a training pool, and optimizing the neural network by using the data in the training pool; after the optimization is completed, adding the remaining data with the highest error-prone degree into a training pool, expanding the categories in the training pool, and further optimizing the neural network by using the training pool on the basis of the neural network obtained by the last training; and continuously performing class expansion on the training pool until the whole data set enters the training pool to obtain a final optimized neural network model. The method is suitable for neural network model optimization based on multi-class datasets in supervised learning, and has good effect and robustness for various complex conditions.