In order to reduce the impact of disguised missing value on data analysis, this paper proposes a new algorithm - The Detection Algorithm for Disguised Missing Value Based on Filter-Kmeans. The algorithm identifies mainly disguised missing value by the clustering effect, and is applied to the data set with certain probability of data points. Firstly, the suitable data object points are selected using the silhouette coefficient method and Bernoulli’s law of large numbers. And then the weighted average distance is used to control the cluster traversal. Finally, the filtering operation is performed during the process of cluster traversal. According to the experimental results, the algorithm achieves better improvement in the precision ratio, recall ratio and F1-measure on the open dataset.