In the dynamic field of consumer electronics， understanding user behavior is crucial for product innovation and raising user satisfaction. Therefore， a groundbreaking multi-clustering method was proposed that combines deep learning with tensor decomposition to address challenges of data analysis and mining. Firstly， high-level features were extracted from complex heterogeneous datasets， such as for datasets of various sensors and user interactions in modern devices， deep neural networks were used to encapsulate diverse features of data sources. Secondly， tensor decomposition techniques were applied to feature extraction and clustering analysis， thereby treating each data source as a different modality within a data tensor to reveal latent structure and patterns of the data source. Finally， experiments were carried out on a dataset obtained in collaboration with an e-commerce platform， covering tens of thousands of customers. Empirical results demonstrate that the proposed tensor decomposition algorithm integrated with Convolutional Neural Network （CNN） performs well on consumer electronics-related datasets， with all accuracies over 0.7 and outstanding scores in key metrics such as purity， Adjusted Rand Index （ARI）， and Normalized Mutual Information （NMI）， confirming effectiveness of the proposed method in capturing intrinsic structure and similarity of data； compared with existing methods such as Dynamic Multi-Clustering Routine （DMCR） method， Deep Multi-Modal Clustering （DMMC） method， and FAST-CNN， the proposed method shows significant advantages on multiple evaluation metrics， verifying its superiority over the comparative methods in terms of accuracy and stability， and its advantages in uncovering underlying data principles and interrelationships between heterogeneous data.