Objective · To construct bacterial signaturesanalyzing fecal metagenomics for the screening and diagnosis of colorectal cancer (CRC). Methods · A total of 285 samples were included in the study. Diagnostic models for CRC according to six different machine learning algorithms were developed using the featured bacteria selectedrandom forest algorithm, and validated in validation sets. Results · Nine bacteria that differentiated CRC and the control were identified, with which 6 models were established. The best model was random forest model, with an accuracy of 0.847 7 in the training set. Its accuracy in two test sets was 0.815 8 and 0.734 4, respectively. The area under curve (AUC) of receiver operating characteristic of the random forest model in the set including all samples was 0.894. Conclusion · Bacterial signatures based on random forest algorithm for the diagnosis of CRC can differentiate patients with CRC and the control effectively, which suggests the potential clinical value of the bacterial signatures.