Analyzing three-dimensional excitation-emission matrix (3D-EEM) spectra through machine learning models has drawn increasing attention, whereas the reliability of these machine learning models remains unclear due to their “black box” nature. In this study, the convolutional neural network (CNN) for classifying numbers of fluorescent components in 3D-EEM spectra was interpreted by gradient-weighted class activation mapping (Grad-CAM), guided Grad-CAM, and structured attention graphs (SAGs). Results showed that the original CNN classifier with high classification accuracy may make a classification based on misleading attention to the non-fluorescence area in 3D-EEM spectra. By removing Rayleigh scatterings in 3D-EEM spectra and integrating convolutional block attention module (CBAM) in CNN classifiers, the correct attention of the trained CNN classifier with CBAM greatly increased from 17.6% to 57.2%. This work formulated strategies for improving CNN classifiers associated with environmental fields and would provide great help for water determination in both natural and artificial environments.

通过机器学习模型分析三维激发发射矩阵（3D-EEM）光谱已引起越来越多的关注，但由于其“黑匣子”性质，这些机器学习模型的可靠性仍不清楚。在本研究中，通过梯度加权类激活映射 (Grad-CAM)、引导 Grad-CAM 和结构化注意力图 (SAG) 来解释用于对 3D-EEM 光谱中荧光成分数量进行分类的卷积神经网络 (CNN)。结果表明，原始的CNN分类器具有较高的分类精度，可能会基于对3D-EEM光谱中非荧光区域的误导性关注而进行分类。通过去除 3D-EEM 谱中的瑞利散射并在 CNN 分类器中集成卷积块注意力模块 (CBAM)，训练有 CBAM 的 CNN 分类器的正确注意力从 17.6% 大幅提高到 57.2%。这项工作制定了改进与环境领域相关的 CNN 分类器的策略，将为自然和人工环境中的水测定提供很大的帮助。