In view of the low accuracy of the traditional pattern recognition algorithm for qualitative and quantitative detection of mixed gases, a novel algorithm of hybrid gas qualitative identification and concentration quantitative detection based on machine learning is proposed. The algorithm constructs the feature map of sensor array data first, then uses the convolutional neural network ( CNN) to extract features from feature maps. According to the feature map after feature extraction, different branches are used to identify different gases, then the species of gases and their concentration range were obtained; based on the results of gas identification, the kernel principal component analysis (KPCA) and gradient boosting decision tree (GBDT) were used to estimate the composition of the mixed gases quantitatively. Finally, this paper used the dataset of sensor array of mixed gases of Machine Learning Database of the University of California to verify the results. Experimental results show that the accuracy of the algorithm in the qualitative recognition of ethylene and methane reaches 98. 7% and the average relative error of quantitative detection was less than 4. 1%. Compared with the traditional pattern recognition algorithm, the machine learning based mixed gas detection algorithm that proposed has higher accuracy and stronger generalization ability.