The data dimensionality reduction is the main method that can enhance the outliers mining efficiency based on higher- dimension data set. The research of detection of outliers based on manifold learning is proposed after analyzing the advantages and disadvantages of the classical outlier mining algorithm in the paper. Local Linear Embedding algorithm (LLE) is an effective technique for nonlinear dimensionality reduction in manifold learning. Compared with other dimensionality reduction algorithms, the advantage of the local Linear Embedding algorithm is that it only defines unique parameter, i. e. number of nearest neighbours. With the idea of Local Linear Embedding, the algorithm can select optimal parameter and regulate the distance among data set after data dimensionality reduction, so as to improve efficiency of detection of outliers. The algorithm determines weighted values by discretion formula of weighted outliers. Through these weighted values, the experts can identify the outliers easily. Simulation results illustrate that this algorithm is very efficient. Moreover, our method has the advantage of simple parameter estimation and low parameter sensitivity. Our method gives a new way for the solution of detection of outliers.