Background: Colorectal cancer liver metastasis (CRLM) and hepatocellular carcinoma (HCC) are both high incidence tumors in China. In certain poorly differentiated cases they can exhibit comparable imaging and pathological characteristics, which impedes accurate clinical diagnosis. The use of protein-based techniques with tissue slides offers a more precise means to assess pathological changes and has the potential to assist with tumor diagnosis. Methods: A simple in situ protein digestion protocol was established for protein fingerprint analysis of paraffin-embedded tissue slide samples. Additionally, machine learning techniques were employed to construct predictive models for CRLM and HCC. The accuracy of these models was validated using tissue slides and a clinical database. Results: Analysis of differential protein expression between CRLM and HCC groups reliably identified 977 proteins. Among these, 53 were highly abundant in CRLM samples and 57 were highly abundant in HCC samples. A prediction model based on the expression of six proteins (CD9, GSTA1, KRT20, COL1A2, AKR1C3, and HIST2H2BD) had an area under curve (AUC) of 0.9667. This was further refined to three proteins (CD9, ALDH1A1, and GSTA1) with an AUC of 0.9333. Conclusions: Tissue slide proteomics can facilitate accurate differentiation between CRLM and HCC. This methodology holds great promise for improving clinical tumor diagnosis and for identifying novel markers for challenging pathological specimens.