利用光谱仪器检测土壤中重金属时，由于仪器分辨率较低，峰位相近元素的特征峰会产生重叠。光谱重叠峰严重影响定量分析结果的准确性，为了得到准确的重金属含量需要进行光谱重叠峰分解。提出利用非洲秃鹫算法优化卷积神经网络（AVOA-CNN）的重叠峰解析方法。首先，利用高斯函数模型模拟出150个双高斯含噪光谱重叠峰和43个三高斯含噪光谱重叠峰，选择不同小波基函数进行光谱数据去噪，以信噪比和均方根误差（root mean square error, RMSE）为评价指标，最终确定coif 3小波基函数，使用导数法进行光谱重叠峰预处理。然后，使用AVOA-CNN获得卷积神经网络（convolutional neural net-work, CNN）预测结果，解析结果表明：AVOA-CNN成功分解重叠峰且准确率高，双高斯光谱重叠峰和三高斯光谱重叠峰参数（峰强度，峰位，峰宽）的最大相对误差平均值分别为3.15%和5.90%。最后对比麻雀搜索算法优化CNN、CNN与AVOA-CNN，结果显示AVOA-CNN模型预测准确率最高。

Due to the low resolution of spectroscopic instrument, the characteristics of elements with similar peak position overlap when detecting heavy metals in soil. Spectral overlapping peaks seriously affect the accuracy of quantitative analysis results. In order to obtain accurate heavy metal content, spectral overlapping peaks need to be decomposed. The African vulture algorithm was used to optimize the overlapping peaks of convolutional neural networks (AVOA-CNN). Firstly, 150 double Gaussian overlapping peaks and 43 triple Gaussian overlapping peaks with noise were simulated by Gaussian function model. Different wavelet basis functions were selected for spectral data denoising. With signal-to-noise ratio and root mean square error (RMSE) as evaluation indexes, coif 3 wavelet basis function was finally determined, and derivative method was used to pretreat spectral overlapping peaks. Then, AVOA-CNN was used to obtain the convolutional neural network (CNN) prediction results. The analytic results show that AVOA-CNN can decompose the overlapping peaks successfully and with high accuracy, and that the parameters of the double and triple Gaussian overlapping peaks (peak intensity, peak location, Peak width) are 3.15% and 5.90%, respectively. Finally, by comparing the sparrow search algorithm to optimize CNN, CNN and AVOA-CNN, the results show that the AVOA-CNN model has the highest prediction accuracy.