A gesture recognition method, comprising: obtaining an original point cloud data set, wherein the original point cloud data set comprises a plurality of pieces of point cloud data, the plurality of pieces of point cloud data correspond to a plurality of reflected signals, and the reflected signals correspond to radar signals transmitted by a radar apparatus; and filtering the original point cloud data set to obtain a first point cloud data set, wherein the first point cloud data set comprises a plurality of frames of first point cloud subsets, the first point cloud subset comprises a first cluster center, the first cluster center is a cluster center of a plurality of pieces of point cloud data in the first point cloud subset, a maximum horizontal distance between any two first cluster centers meets a first preset condition, and duration of the first point cloud data set meets the first preset condition; performing gesture recognition based on a feature value of point cloud data in the first point cloud data set, wherein the feature value comprises at least one of a coordinate value, a distance, or a velocity of point cloud data in a spatial coordinate system; wherein the performing gesture recognition based on the feature value of the point cloud data in the first point cloud data set comprises: performing clustering processing on the first point cloud data set to generate a second point cloud data set, wherein the second point cloud data set comprises a plurality of frames of second point cloud data subsets, the second point cloud data subset is a subclass with a largest amount of point cloud data after clustering in the corresponding first point cloud subset, the second point cloud data subset comprises a second cluster center, and a bounding box size of the second cluster center is less than or equal to a bounding box size of the first cluster center; and performing gesture recognition based on a feature value of point cloud data in the second point cloud data set and wherein before the performing clustering processing on the first point cloud data set to generate the second point cloud data set, the method further comprises: normalizing the first point cloud data set when a quantity of frames in the first point cloud data set is inconsistent with a preset quantity of frames, wherein the quantity of frames in the first point cloud data set before normalization is expanded or decreased to the preset quantity of frames, a quantity of frames in the first point cloud data set after normalization is the preset quantity of frames, and the preset quantity of frames is determined by a frame rate of the radar signals transmitted by the radar apparatus. obtaining an original point cloud data set, wherein the original point cloud data set comprises a plurality of pieces of point cloud data, the plurality of pieces of point cloud data correspond to a plurality of reflected signals, and the reflected signals correspond to radar signals transmitted by a radar apparatus; and filtering the original point cloud data set to obtain a first point cloud data set, wherein the first point cloud data set comprises a plurality of frames of first point cloud subsets, the first point cloud subset comprises a first cluster center, the first cluster center is a cluster center of a plurality of pieces of point cloud data in the first point cloud subset, a maximum horizontal distance between any two first cluster centers meets a first preset condition, and duration of the first point cloud data set meets the first preset condition; performing gesture recognition based on a feature value of point cloud data in the first point cloud data set, wherein the feature value comprises at least one of a coordinate value, a distance, or a velocity of point cloud data in a spatial coordinate system; wherein the performing gesture recognition based on the feature value of the point cloud data in the first point cloud data set comprises: performing clustering processing on the first point cloud data set to generate a second point cloud data set, wherein the second point cloud data set comprises a plurality of frames of second point cloud data subsets, the second point cloud data subset is a subclass with a largest amount of point cloud data after clustering in the corresponding first point cloud subset, the second point cloud data subset comprises a second cluster center, and a bounding box size of the second cluster center is less than or equal to a bounding box size of the first cluster center; and performing gesture recognition based on a feature value of point cloud data in the second point cloud data set and wherein before the performing clustering processing on the first point cloud data set to generate the second point cloud data set, the method further comprises: normalizing the first point cloud data set when a quantity of frames in the first point cloud data set is inconsistent with a preset quantity of frames, wherein the quantity of frames in the first point cloud data set before normalization is expanded or decreased to the preset quantity of frames, a quantity of frames in the first point cloud data set after normalization is the preset quantity of frames, and the preset quantity of frames is determined by a frame rate of the radar signals transmitted by the radar apparatus.