Cancer is a high-risk, highly heterogeneous, and complex disease, and precisely identifying cancer subtypes is crucial for guiding personalized treatment and improving patients’prognosis. To this end, a rational multiomics data processing method is proposed to improve the precision of cancer subtype identification. This method primarily utilizes a feature selection model to reasonably rank omics data characterized by high dimensionality and small sample sizes, and integrates a cancer subtype identification model for data cleaning, aiming to enhance the precision of cancer subtype identification. Through the validation of three types of cancer data and three cancer subtype identification models, this processing method effectively enhances the identification precision of the multi-omics cancer subtype identification model. Finally, the prospect of this work is put forward, which provides a new perspective for the research and development of precise subtype identification.