Abstract: Classical convolutional Neural Networks(CNNs) have been successfully widely used in the image application field.However,when images undergo rotations or scaling transformations,the relative positions and scales of features change,presenting challenges for traditional CNNs in extracting stable and invariant image features.To address this issue,this paper introduces a Con-volutional Enhanced Adaptive Classification Model(CEACM),which consists of two parts:feature extraction and classifier design.In the feature extraction stage,a feature invariant layer is used to enhance the CNN,applying rotational transformations to enhance the convolutional neural network features,allowing the model to extract stable and representative features from the input data.In the classifier part,an adaptive enhancement model based on Particle Swarm Optimization(PSO) is proposed,where the PSO algorithm is used to optimize the weights of the classifier to avoid local optima,thus improving the model’s generalization and classification performance.Finally,the model’s performance is evaluated using a series of image datasets.Experimental re-sults indicate that the proposed CEACM outperforms traditional machine learning models and a series of improved models in terms of classification effectiveness.

Key words: Data augmentation, Convolutional neural network, Adaptive boosting, Particle swarm optimization