[19] used the expanded principal component analysis combined with PARAFAC analysis to distinguish pure olive oil and virgin olive oil, founding three classes of possible compounds. [PERSON] et al. [20] used the PCA method to track pollutants in the drinking water treatment process. [PERSON] et al. [21] used four machine learning algorithms (i.e., decision tree, k-nearest neighbor, multilayer perceptron, and support vector machine) combined with the PARAFAC analysis method to classify a large number of 3D-EEMs data. In addition, there are also many research papers on 3D-EEMs data combined with deep learning algorithms. For example, [PERSON] [7] understood the 3D-EEMs matrix as an image channel, added X and Y coordinate channels, and established an organic pollutants evaluation model based on a position convolution network (CoordConv) and cosine similarity. [PERSON] [22] used the contour map generated by 3D-EEMs as the training dataset of AlexNet, combined with SVM and PLS (least square method) to judge whether there is forgery in sesame oil, and obtained a 100% recognition accuracy. [PERSON] [8], combined with the analysis results of PARAFAC analysis, predicted the number of components of different kinds of 3D-EEMs data by using the NCNN network (a revolutionary neural network model for the number of fluorescent components) and fitted the components with MCNN network (a revolutionary neural network model for the map of fluorescent components). In addition, [PERSON] [23] used yolo-v3 algorithm to classify and recognize the fluorescent image and used the algorithm to segment the fluorescent region. They analyzed the relationship between heavy metal ions in agricultural residues and fluorescence intensity from the perspective of RGB value, HSV value, and the concentration of agricultural residues. [PERSON] [24] also used the yolo-v3 algorithm combined with the wechat applet to detect and classify fluorescence images and realized a scheme to quickly detect GSH and ADA on site based on an image processing algorithm. Based on the above research, nowadays, in addition to the traditional FRI, PCA, and PARAFAC, etc., there have been many machine learning algorithms and deep learning algorithms to prove the feasibility of the study of 3D fluorescence data. Based on these studies, combined with the analysis results of PARAFAC analysis and VGG16 and VGG11 convolutional neural network, in this paper, we developed a new 3D fluorescence data classification model and 3D fluorescence component maps fitting model. Among them, VGG16 is used for the classification of 3D fluorescence data, which is used to classify the 3D fluorescence from different sources. VGG11 is used for components map fitting, and compared with the MCNN model of [PERSON] et al., this model does not need to create a submodel for each component map but determines the number of submodels based on the maximum number of components of 3D fluorescence, which makes the components fitting model more robust and prevents the overfitting effect of the network. In addition, VGG11 has a better fitting effect by comparing the model results with AlexNet and LeNet (the backbone network used by MCNN). Experiments show that according to the PARAFAC analysis results, the convolutional neural network can predict the category of 3D fluorescence data and fit its specific components map only by inputting a single 3D fluorescence datum. In this way, we don't need to perform complex PARAFAC analysis on 3D-EEMs and can quickly obtain the category and components map of 3D fluorescence data using CNN models only, which provides a reliable technical means in river monitoring, drinking water monitoring, and other scenes that need rapid results.