The invention discloses a content-based matching recommendation method for user personalized products, which overcomes the problem that the recommendation algorithm in the prior art still needs to be improved. The invention includes step 1, user-based random batch sampling method; step 2, content-based user-product matching method: establish a network; according to batch input based on user-based random batch sampling method, orderly user ids are obtained, and a list of product ids is recorded in history , a set of target product id and label, train, adjust, and evaluate the network model on the training set, verification set, and test set respectively; input a specific user and its history, and use the content-based user-product matching network to predict its response to all unwatched Score and sort the movies, and finally output top-N recommendation results. The invention adopts a lightweight neural network, which greatly reduces the training time and training equipment requirements, the sampling process is easy, the model input is more random, and the generalization ability of the prediction result is stronger.