Abstract: The progress of tumor individualized treatment puts forward higher requirements for the accurate diagnosis of tumor tissue-based specific biomarkers. The development of digital pathology provides a basis for the application of artificial intelligence (AI)-assisted diagnostic tools in tumor pathological image analysis. The deep learning algorithm based on a convolutional neural network combines digital pathological images with computer analysis technology, which is an important tool for quantitative evaluation of tumor-tissue biomarkers. This review summarizes the development of AI in histopathology and focuses on the progress of image analysis of molecular biomarkers such as Her-2, Ki-67, and PD-L1. These molecular biomarkers are specificly supported by extensive research and closely related to clinical diagnosis and treatment. Studies have shown that AI-assisted tumor diagnosis has the advantages of strong objectivity and high repeatability. It can obtain the quantitative results of the tumor-tissue biomarkers to overcome the challenges of manual interpretation and improve the accuracy of diagnosis of pathologists. The development of AI-based analysis tools of tumor-tissue biomarkers is an important method to build intelligent and accurate tumor diagnosis and treatment systems of the future.