6. The video classification method according to claim 4 , wherein the sample video library comprises at least two videos, and the sample video library comprises videos in at least two categories; and wherein the generating the descriptive vector corresponding to the video in the sample video library comprises: obtaining a screening result set according to screening results of the motion phrases corresponding to the videos in different categories in the sample video library; and generating, according to the screening result set, the descriptive vector corresponding to the videos in the sample video library.
4. The method according to claim 3 , wherein a motion phrase in the motion phrase set comprises one motion atom in the motion atom set, and the method further comprises: acquiring a motion atom unit π(A,t,σ); and acquiring a representativeness parameter Rep(P 1 ,c) of a motion phrase according to the motion atom unit, wherein Rep ⁡ ( P 1 , c ) = ∑ i ∈ S ⁡ ( P 1 , c ) ⁢ r ⁡ ( V , P 1 )  S ⁡ ( P 1 , c )  , A is a motion atom, t is a time point in a video in the sample video library, σ is a standard deviation of Gaussian distribution, V is a video in the sample video library, P 1 is the motion phrase, r(V,P 1 ) is a response of the motion phrase P 1 with respect to the video in the sample video library, r ⁡ ( V , P 1 ) = min OR i ∈ P 1 ⁢ max π ∈ OR i ⁢ v ⁡ ( V , π ) , ⁢ v ⁡ ( V , π ) = max t ′ ∈ Ω ⁡ ( t ) ⁢ Score ⁡ ( Φ ⁡ ( V , t ′ ) , A ) · N ⁡ ( t ′ | t , σ ) , wherein OR i refers to calculation of responses between the video in the sample video library and the motion atom units that are adjacent in time, S(P 1 ,c) denotes a set of videos, with respect to set of videos, a motion phrase has the greatest response, in the sample video library, c is an identifier of a category of a video in the sample video library, Φ(V,t′) denotes a video feature of a segment result that is in a video in the sample video library and that starts from, t′, Score(Φ(V,t′),A) denotes a score obtained by inputting Φ(V,t′) into a support vector machine (SVM) classifier, N(t′|t,σ) denotes Gaussian distribution with a mean t and a standard deviation σ, and Ω(t) denotes an adjacent area with a center t; acquiring a coverage parameter RepSet(Γ 1 c ,c) of the motion phrase; obtaining, according to the coverage parameter RepSet(Γ 1 c ,c) of the motion phrase, a contribution value ΔRepSet(P 1 ,c), which contributes to the coverage parameter, of the motion phrase, wherein RepSet ⁡ ( Γ 1 c , c ) = 1 T c ⁢  ⋃ P 1 ∈ Γ ⁢ S ⁡ ( P 1 , c )  , wherein ΔRepSet(P 1 ,c)=RepSet(Γ 1 c ,c)−RepSet(Γ 1 c −{P 1 },c), T c is a quantity of segments obtained by segmenting a video whose identifier is c and that is in the sample video library, Γ 1 c is the motion phrase set, and an identifier of a category of a video to which the motion atom comprised in the motion phrase belongs is c; and performing the foregoing process for each motion phrase in the motion phrase set; and obtaining a representativeness parameter and a contribution value of each motion phrase in the motion phrase set.