Air quality prediction is of great importance for people’s daily travel. As a new recurrent neural network (RNN) of deep learning, the long short-term memory (LSTM) network demonstrates good prediction ability for time sequence data. However, neural network models generally rely on experience for parameter selection during training and have a long training period, low prediction accuracy, and unreliable prediction results. Considering this, this study proposes a bidirectional LSTM model based on the whale optimization algorithm (WOA), namely, the WOA-BiLSTM model. Specifically, the BiLSTM network can enhance the memory capability of sequence data information by its forward and backward network structure, and WOA can assist the BiLSTM model in finding the optimal network parameters during the training process on the basis of the bubble-net hunting strategy of whales. The model is applied for air quality index (AQI) prediction in Shaanxi Province and compared with BiLSTM and LSTM models separately, and it is found that the proposed model registers the best prediction result with the MAE value of 6.543 3 and R² value of 0.989 9. Therefore, the model is of solid theoretical and practical significance for applications in air quality prediction.