A new method for solving the optimal distribution of thermoelectric load in gas turbine power plants is presented on the basis of deep learning and chaos optimization method. Long-and short-time memory(LSTM)neural network algorithm in deep learning theory is used to establish the energy consumption model for the unit.The non-analytical functional relationship between unit power consumption and electrical load

thermal load and environmental parameters in the model is trained and studied

then the load distribution of the model trained by the deep learning LSTM neural network algorithm is optimized by the chaotic optimization algorithm

and the minimum gas consumption of the unit under the optimal load distribution is obtained. The actual calculation results show that the method is effective and can improve the economy of unit operation.