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Efficient management of image data is essential in the consumer electronics industry. Image clustering has evolved through the utilization of dimensionality reduction and representation learning techniques to extract pertinent features. However, dealing with a broad spectrum of Consumer Electronics Image categories poses challenges due to complex data distribution and cluster density. To tackle these issues, a contrastive learning-based hyper-multiclass deep image clustering approach has been introduced. This model comprises three main steps. Firstly, to accommodate the diverse range of consumer electronics images, the feature model undergoes initial training using enhanced contrastive learning to ensure uniform cluster distribution. Secondly, semantic similarity is leveraged to acquire instance semantic closest neighbor data employing a multi-perspective methodology. Lastly, instances along with their closest neighbors serve as self-supervised information to train the clustering model. Ablation and comparative analyses have demonstrated the efficacy of this approach. It effectively achieves uniform distribution of clusters within the mapped space and consistently extracts semantic nearest neighbor information. Comparative experiments were conducted on benchmark datasets, specifically the ImageNet G200 and G1000 class datasets, within the consumer electronics context. Results indicate a significant improvement in accuracy, with enhancements of 10.6% and 9.2%, respectively, surpassing the performance of existing advanced methods.