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In order to enhance the feature classification performance of localy linear embedding (LLE) algorithm for face recognition, the paper introduced minimum spanning tree algorithm and proposed a new local linear embedding algorithm with the dynamically determining neighborhood parameter. The new algorithm could automatically determine the neighborhood of the data point by using the single-chain clustering and further optimization algorithm, improved the insufficient of local linear embedding algorithm fixed neighborhood, as well as deal with the reality of non-uniform failures of source datasets. It used the improved algorithm and support vector machine (SVM) classifier for face recognition to get a higher upgrade of the average recognition rate on the ORL and YALB face database. And the simulation result shows the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]