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Extreme learning machine (ELM) has been proved to be an effective pattern classification and regression learning mechanism by researchers. However, its good performance is based on a large number of hidden layer nodes. With the in-crease of the nodes in the hidden layers, the computation cost is greatly increased. In this paper, we propose a novel algorithm, named constrained voting extreme learning machine (CV-ELM). Compared with the traditional ELM, the CV-ELM determines the input weight and bias based on the differences of between-class samples. At the same time, to improve the accuracy of the pro-posed method, the voting selection is introduced. The proposed method is evaluated on public benchmark datasets. The experi-mental results show that the proposed algorithm is superior to the original ELM algorithm. Further, we apply the CV-ELM to the classification of superheat degree (SD) state in the aluminum electrolysis industry, and the recognition accuracy rate reaches 87.4％, and the experimental results demonstrate that the pro-posed method is more robust than the existing state-of-the-art identification methods.