Extreme Learning Machine (ELM) has been widely used in many applications due to its fast convergence and good generalization performance. However, the training speed will slow down or ELM will make error when the number of the training samples reaches a certain scale. Conjugate gradient algorithm was introduced into the ELM model instead of the generalized inverse. The experimental results show that, under the condition of the same generalization accuracy, conjugate gradient-based ELM has faster training speed than that of ELM with matrix inversion. Because conjugate gradient-based ELM do not need to calculate the generalized inverse of a hidden layer output matrix, while most of the generalized inverse calculations depend on the matrix Singular Value Decomposition (SVD), which has low efficiency for a high-order matrix. It has been proved that the conjugate gradient algorithm can find the solution through iteration with finite steps, so the conjugate gradient-based ELM algorithm has faster training speed and is also suitable for processing big data.