An improved AdaBoost reinforcement learning algorithm is proposed for distinguishing the breath signals of healthy patients and liver cancer patients. First， the breath signals of volunteers， including healthy controls and liver cancer patients， are collected and their main features are extracted by Relief algorithm. Then， based on Stacking model， several groups of base classifiers are trained by traditional machine learning algorithms and some sub-classifiers are then constructed. To reduce the influence of training samples on the classifier performance， a K-fold crossover is applied， and k base classifiers could be successively obtained to form a base classifier group. Further， the prediction results of this base classifier group， i.e.， sub-classifiers on the test set， are obtained by the voting method. Then， according to the prediction error rate of each sub-classifier on the training set， the training set is updated and the weight coefficients of each sub-classifier are obtained according to the prediction error rate of each sub-classifier on the training set. Finally， the prediction results of multiple sub-classifiers are weighted and combined to obtain the final prediction results. Experimental results show that the improved AdaBoost algorithm can achieve an accuracy of about 90% and the specificity and precision are more than 95% in discriminating the breath of liver cancer from the breath of healthy controls. Compared with the traditional AdaBoost algorithm， the proposed algorithm has significantly lower error rate and improved robustness when used for liver cancer breath detection. Therefore， the improved AdaBoost algorithm can effectively improve the accuracy of liver cancer breath identification， which is important for the research of identifying liver cancer by breath for early diagnosis.