The invention belongs to the technical field of smell analysis, and discloses a self-organizing map based artificial olfactory system online correction sample generation method. The method comprises an initial training stage and an online updating stage. In the initial training stage, a self-organizing map neural network multi-layer structure is constructed according to the number of sample categories, self-organizing map neural network neuron weight initialization is performed according to training samples, the self-organizing map neural network neuron weights are enabled to act as an initial training sample set; and in the online updating stage, and adjustment is performed on local region neuron weights by using a test sample according to a classification result of a follow-up classifier. The current self-organizing map neural network neuron weights are enabled to act as an online training sample set to perform online correction on a mode recognition method. Results show that the method can improve the long-term drift resisting capacity of an artificial olfactory system under an online working condition, can automatically generate a correction sample in the online working process and provides guarantee for the artificial olfactory system to realize automatic online correction.