The dimensionality reduction performance of Local Linear Embedding algorithm LLE is closely related to the manifold structure mined. However, the manifold structure mined by LLE is singular and sensitive to the selection of neighborhood parameters, making it difficult to extract a comprehensive local structure of the manifold, which limits its dimensionality reduction performance.Therefore, this article proposes an adaptive local linear embedding algorithm based on multiple information fusion MIF-ALLE. MIF-ALLE firstly uses tangent space approximation criterion to adaptively select neighborhood parameters to obtain more accurate local neighborhood; Then, the angle information of Tangent space contained in the local neighborhood is fused with the local linear information to mine a more comprehensive local structure of the manifold and reduce the deviation of local low dimensional embedding; Finally, the experimental verification is carried out on the bearing data set published and the bearing data set extracted from the laboratory. The experimental results show that MIF-ALLE can mine more comprehensive manifold structures, extract more significant features, and achieve bearing fault diagnosis accuracy of up to 100%.