A method for classifying a face image, comprising: acquiring (101; 201; 301; 401; 501) a face image; extracting (102; 202; 302; 402; 502) a face feature from the face image; being characterized in that, the method further comprises: when a global clustering time point is not up, determining a category of the face feature based on categories of existing face features contained in a face feature collection through a neighbor face feature algorithm; wherein the neighbor face feature algorithm is a K-nearest neighbor algorithm or an approximate nearest neighbor algorithm, and wherein determining the category of the face feature comprises: determining the total number of existing face features in the face feature collection; obtaining similarity degrees between the face feature and the existing face features in the face feature collection and ranking the existing face features based on the similarity degrees, when the total number is smaller than or equal to a second number threshold; determining the category of the face feature based on categories of a third preset number of top-ranked existing face features; and determining (104; 208; 313; 409; 510) the category of the face feature as a category of the face image; wherein, the method further comprises: when the global clustering time point is up, adding the face feature into the face feature collection, clustering existing face features contained in the face feature collection to obtain a clustering result, and determining the category of the face image corresponding to the face feature based on the clustering result. acquiring (101; 201; 301; 401; 501) a face image; extracting (102; 202; 302; 402; 502) a face feature from the face image; being characterized in that, the method further comprises: when a global clustering time point is not up, determining a category of the face feature based on categories of existing face features contained in a face feature collection through a neighbor face feature algorithm; wherein the neighbor face feature algorithm is a K-nearest neighbor algorithm or an approximate nearest neighbor algorithm, and wherein determining the category of the face feature comprises: determining the total number of existing face features in the face feature collection; obtaining similarity degrees between the face feature and the existing face features in the face feature collection and ranking the existing face features based on the similarity degrees, when the total number is smaller than or equal to a second number threshold; determining the category of the face feature based on categories of a third preset number of top-ranked existing face features; and determining (104; 208; 313; 409; 510) the category of the face feature as a category of the face image; wherein, the method further comprises: when the global clustering time point is up, adding the face feature into the face feature collection, clustering existing face features contained in the face feature collection to obtain a clustering result, and determining the category of the face image corresponding to the face feature based on the clustering result. when a global clustering time point is not up, determining a category of the face feature based on categories of existing face features contained in a face feature collection through a neighbor face feature algorithm; wherein the neighbor face feature algorithm is a K-nearest neighbor algorithm or an approximate nearest neighbor algorithm, and wherein determining the category of the face feature comprises: determining the total number of existing face features in the face feature collection; obtaining similarity degrees between the face feature and the existing face features in the face feature collection and ranking the existing face features based on the similarity degrees, when the total number is smaller than or equal to a second number threshold; determining the category of the face feature based on categories of a third preset number of top-ranked existing face features; and determining (104; 208; 313; 409; 510) the category of the face feature as a category of the face image; determining the total number of existing face features in the face feature collection; obtaining similarity degrees between the face feature and the existing face features in the face feature collection and ranking the existing face features based on the similarity degrees, when the total number is smaller than or equal to a second number threshold; determining the category of the face feature based on categories of a third preset number of top-ranked existing face features; and determining (104; 208; 313; 409; 510) the category of the face feature as a category of the face image; wherein, the method further comprises: when the global clustering time point is up, adding the face feature into the face feature collection, clustering existing face features contained in the face feature collection to obtain a clustering result, and determining the category of the face image corresponding to the face feature based on the clustering result.