Missing data reduces data availability. Prediction of missing data becomes very important. A prediction algorithm named ISSA-DELM(Improved Sparrow Search Algorithm optimized Deep Extreme Learning) was proposed to solve the problem of missing data. First of all, singer chaotic map, Cauchy-Gaussian mutation strategy and cosine weight factor combined with sparrow search algorithm. Secondly, the input weights and biases of the autoencoders in each extreme learning machine in the deep extreme learning machine are optimized by ISSA. Then ISSA-DELM is applied to predict missing data. The experimental results show that, ISSA-DELM has strong stability and the highest prediction accuracy compared with SSA-DELM、Particle Swarm Optimization DELM( PSO-DELM)、DELM in the case of small data volume and low miss rate. The evaluation indexes, such as RMSE, MAE and the coefficient of determination are better than the compared algorithms.