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Due to the drawbacks of parameter optimization in Extreme learning Machine, an Improved Artificial Bee Colony was proposed to optimize ELM classification model. In the IABC algorithm, the solution update strategy pool was used to replace the fixed update strategy; Optimized the search mode of scouts; The initial random number sequence generated by Kent mapping for better uniformity. In the classification data set, IABC-ELM classification model was compared with the ELM and PSO-ELM classification model. IABC-ELM model obtains the best classification result and the lowest output weight norm. The results show that the classification performance of IABC-ELM model is significantly better than that of the contrast model. Confirmed the validity and superiority of the IABC algorithm to optimize the ELM classification model.