Rabbit liver VX2 tumor is a tumor model that can grow rapidly in various organs, such as liver, lung, rectum, etc., and is often used in tumor research. In this paper, using high-near-infrared spectrum technology to four rabbits VX2 liver tumor and normal tissue in vivo and in vitro reflection spectrum detection, then respectively the Two categories based on support vector machine (normal liver tissue and liver VX2 tumor tissue) and Four categories (not bleeding living normal liver tissue, not living liver VX2 tumor tissue bleeding, bleeding in vitro normal liver tissue and hemorrhage in vitro liver VX2 tumor tissue). According to its spectral reflection curve characteristics, the data in the range of 400~1 800 nm are selected as characteristic variables. In order to further improve the classification accuracy, the kernel parameter g and penalty factor c of the support vector machine was optimized by using a 50 fold cross-validation and genetic algorithm, respectively. The optimization parameters and classification results of the 50-fold cross-validation are as follows: penalty parameter c of the dichotomy optimization is 4, kernel parameter g is 0.125 0, and the accuracy of the correction set and prediction set reaches 100%. The optimized parameters c and g are 8 and 0.121 1, and the accuracy of the correction set and the prediction set are 99.242 4% and 93.33 3%, respectively. The optimized parameters and results of the genetic algorithm are as follows: the optimized parameters c and g in dichotomy are 0.845 6 and 0.062 5, respectively, and the accuracy of Two categories, the correction set and the prediction set, is agreed to reach 100%.The optimized parameter C in the Four categories was 5.530 7 and g was 0.068 5, and the accuracy of the correction set and the prediction set reached 99.242 4% and 100%, respectively. The results show that the two optimization methods have achieved good results, and the genetic algorithm is more accurate in the classification of the Four categories. In order to further improve the speed of the algorithm, the method of variable selection at intervals was adopted to reduce the characteristic variables continuously. Finally, a variable was selected for every 100 nm spectral segment, and a total of 14 spectral segments were selected as the characteristic variables. Parameters of support vector machine were optimized by using genetic algorithm for the classification was studied, the results show that the Two categories and Four categories of both results of the calibration set and prediction set were 99.242 4%, and the running time of 11.4 s and 20.0 s respectively, and choosing all band running time: 340.3 s and 491.0 s compared to how spectroscopy can be in the identification of hepatic VX2 tumor tissue and normal liver tissue. The classification accuracy rate can reach more than 99%, and the running time shorten a lot. Therefore, it also lays a foundation for realising rapid real-time online detection and classification of tumor tissues in the future clinical tumor diagnosis with multi-spectrum technology, showing great application potential.