The coiling temperature control accuracy and coiling hit rate of hot strip rolling are low due to the influence of strong nonlinearity and time variation. This paper proposes a method to optimize long shortterm memory (LSTM) neural network based on improved whale algorithm. The improved Whale optimization algorithm of niche technologymixed mutation strategy (NMWOA) was obtained by combining adaptive parameter optimization and hybrid mutation strategy with niche technology. The coiling temperature prediction model of LSTM optimized by improved whale algorithm was established and compared with other models. Simulation results show that the NMWOA algorithm has better search ability and optimization accuracy among the 10 test functions compared with other advanced algorithms. In the prediction of the coiling temperature model, compared with the other four models, the NMWOALSTM model has a high precision hit rate of 9750%, which improves the prediction accuracy of the coiling temperature.