In this study, a retrieval method based on complex Flex-Bootstrap and multi-convolution neural network (Multi-CNN) and deep neural network (DNN) fusion model is proposed to solve the problems of low efficiency and low accuracy of traditional similarity matching retrieval methods due to single sample and unbalanced data in protein mass spectrometry data retrieval research. Here, we compared our proposed method with the DNN model, CNN, and DNN fusion model. Furthermore, the Flex-Bootstrap method combined with the Multi-CNN and DNN fusion model has achieved promising results when applied in the prediction of protein mass spectrum data types. Experimental results revealed that the test set's accuracy was increased to 98.82%, and the loss function value was reduced to 0.0397. Therefore, this model not only effectively solves the problem of underfitting in data retrieval using the DNN model and CNN and DNN fusion model, but also enhances the accuracy of prediction and the search efficiency of the mass spectrometry database.