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Due to its fast learning and generalization performance, etc, extreme learning machine has become the mainstream of research in today's field of pattern recognition. However, owing to the poor stability of the algorithm, the data set often vulnerable to noise, causing the classification results are not very significant in the practical application. Therefore, in order to improve the accuracy of classification of extreme learning machine, aiming at to solve the problem of noise and outliers in the data set samples, a robust extreme learning machine algorithm is presented based on angle optimization. This method using the principle of the robust activation function of angle optimization, firstly, reduces the impact of outliers on the classification algorithm to maintain the overall structure information of data set samples to achieve better denoising effect. Secondly, it can also effectively avoid the question which is the inaccurate solving of hidden nodes output matrix, and further enhance the generalization performance of extreme learning machine. The experimental results of the application of universal image database show that the proposed algorithm compared with other algorithms has better robustness and higher recognition rate.