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Power load forecasting is a key means for transmission network expansion,planning and reasonable power dispatch.According to the nonlinear and complex characteristics of power load time series, a power load prediction model ISSA-WNN is proposed, which combines wavelet transform and improved sparrow search algorithm to optimize wavelet neural network. The improved sparrow search algorithm is designed to optimize the initial value of the key parameters of the wavelet neural network, which can effectively solve the problem that the gradient parameter adjustment is easy to fall into local optimum and sensitive to the initial value of parameters, and improving the model learning ability. The standard sparrow search algorithm is improved by introducing Logistic-Tent hybrid chaotic population initialization, discoverer/watcher adaptive update, follower variable logarithm spiral update and Gauss-Cauchy hybrid mutation strategy to improve the optimization ability of the algorithm. The wavelet transform is used to decompose and reconstruct the load sample to reduce the disorder and volatility of the load time sequence. On this basis, a new power load prediction model ISSA-WNN is constructed. The experimental results show that compared with the standard wavelet neural network model WNN and standard sparrow search algorithm optimizing wavelet neural network model SSA-WNN, the average absolute percentage error and root mean square error index values of the prediction model ISSA-WNN can be reduced by 18.42% and 21.21% on average, with stronger fitting ability and more stable prediction performance.