Long-term energy consumption data recording and analysis help to identify the trends and patterns of energy consumption, providing important references for formulating carbon reduction strategies. The energy consumption data of key carbon emission monitoring users involves various types. It is not only necessary to conduct clustering analysis on the users' energy consumption data, but also to study more precise clustering result visualization methods. To this end, a Fuzzy C-Means algorithm based on Tent Chaotic Sequence Grey Wolf Optimization(TGWO-FCM) is proposed to analyze the users' energy consumption data. The clustering centers of users' energy consumption data are regarded as grey wolf individuals for optimization, which solves the shortcomings of the FCM algorithm being sensitive to the initial clustering center positions and easily falling into local optimum. The data dimensionality reduction method of Uniform Manifold Approximation Projection(UMAP) is adopted to reduce the complexity of energy consumption data, mapping high-dimensional energy consumption data to two-dimensional or three-dimensional spaces to achieve intuitive visualization of the data. Experimental results show that the method proposed in this paper can classify users with similar energy consumption patterns into the same category, not only revealing the differences in energy consumption patterns among users, but also providing a scientific basis for formulating targeted energy-saving and emission-reduction policies.