Nonferrous metals are important commodities, and it is of great significance for policy makers and investors to accurately predict their price changes. Nevertheless, because the price of nonferrous metals present drastic fluctuations, developing a robust price prediction method is a tricky task. In this research, a hybrid model based on discrete wavelet transform (DWT), bidirectional long short-term memory (BiLSTM) and residual network (ResNet) is constructed for nonferrous metals price prediction. The hyper-parameters of the hybrid neural network are searched by grey wolf optimization (GWO) algorithm. Configuring reasonable parameters, which enhances the final prediction effect. Additionally, behind the second hidden layer, the low and high dimensional features are fused to prevent the degradation of the model. The original sequence is processed by DWT technology, then the sequence is reconstructed, which is beneficial to capture the essential trend. The experimental results show that the proposed BiLSTM-ResNet-GWO-DWT model is more accurate compared with the other benchmark models, which provides an effective reference significance.