In order to identify key genes related to energy metabolism in ulcerative colitis (UC), the expression of energy metabolism-related genes was extracted from the GSE87466 dataset and enriched by differential analysis, and the key genes were identified by using the least absolute shrinkage and selection operator (LASSO) algorithm and support vector machine recursive feature elimination (SVM-RFE) algorithm to identify the key genes of UC energy metabolism, and then enrichment analysis, immune infiltration analysis, prediction of key genes targeting drugs, and construction of ceRNA network were performed on the key genes. Finally, GSE75214 was used as a validation set to verify the expression of key genes. The results show that a total of 32 genes related to energy metabolism are screened, and 5 key genes (SLC16A1, ACSF2, NR1H4, CHST11 and CBR3) are identified by LASSO and SVM algorithms. The single gene enrichment results show that the key genes are involved in the development of UC through glycolysis/glucose de novo, butyric acid metabolism, and pyruvate metabolism. The validation set GSE75214 on the key genes reveals that the expressions are all differential, providing new ideas and directions for the treatment of ulcerative colitis from the perspective of energy metabolism.