The invention relates to a grape wine classification method based on Bayesian optimization and an electronic nose, and the method comprises the following steps: S1, employing a LightGBM algorithm, employing a Leaf-wise tree building method, finding a leaf with the maximum splitting gain from all current leaves each time during tree building, then splitting, and repeating the above steps; the LightGBM uses the maximum tree depth to prune the tree, and excessive fitting is avoided; S2, building a Bayesian optimization algorithm; S3, building a BO-LightGBM, and performing self-optimization adjustment on hyper-parameters of the LightGBM by using a Bayesian hyper-parameter optimization algorithm; enabling bayesian optimization to use a probability model to replace a complex optimization function, introducing the prior of a to-be-optimized target into the probability model, thus the model can effectively reduce unnecessary sampling. The Bayesian optimization method has the advantages that the Bayesian optimization method determines the optimization method of the next evaluation point by constructing the probability model of the function to be optimized and utilizing the probability model, the most advanced result is achieved on some global optimization problems, and the Bayesian optimization method is a better solution for hyper-parameter optimization.