Considering the problem that online sequential extreme learning machine(OS-ELM) has low efficiency and accuracy when processing incremental data, an online sequential extreme learning machine algorithm based on incremental weighted average(WOS-ELM) is proposed. The principle of this algorithm is weighting model residuals trained with raw data and ones trained with incremental data, using the function got as the cost function of this algorithm, deducing a training model which is able to balance raw data and incremental data, and using raw data to weaken the fluctuation of incremental data. With this principle, the algorithm can raise stability, learning efficiency and accuracy of the OS-ELM. The result of a simulation experiment shows that the proposed algorithm has a higher predicting accuracy and better ability of generalization compared with other algorithms.