工业废水分类是水污染防治和水资源管理的前提和基础，相较于生活污水，工业废水的分类研究相对滞后。水体化学需氧量 (COD) 是衡量水体质量的核心指标，针对现有工业废水COD分类算法中预测精度较低的问题，提出基于门控循环单元 (GRU) 的卷积神经网络 (CNN) 混合模型。该模型首先将紫外-可见光谱法测得的工业废水COD数据进行高斯滤波去噪，然后把去噪后的光谱数据输入CNN模型进行特征提取，最后通过GRU神经网络实现工业废水COD分类。实验结果显示，CNN-GRU分类模型经过200次训练后达到收敛, 分类精度达到99.5%，与长短期记忆方法、GRU方法、CNN-LSTM方法相比，该混合模型的分类精度具有显著优势。

The classification of industrial wastewater is a prerequisite and foundation for water pollution prevention and water resources management. However, compared to domestic sewage, research on industrial wastewater classification is relatively lagging behind. Chemical Oxygen Demand (COD) of water is a core indicator for measuring water quality. To address the problem of low prediction accuracy in existing industrial wastewater COD classification algorithms, a convolutional neural network (CNN) hybrid model based on gated recurrent units (GRU) is proposed. According to the hybrid model, the COD data of industrial wastewater measured by UV-Vis spectroscopy is subjected to Gaussian filtering and denoising at the first, then the denoised spectral data is input into the CNN model for feature extraction, and finally, COD classification of industrial wastewater is achieved using GRU neural network. The experimental results show that the CNN-GRU classification model converges after 200 times of training, with a classification accuracy of 99.5%. Compared with the long short-term memory method, the GRU method, and the CNN-LSTM method, the classification accuracy of CNN-GRU method has a significant advantage.