An audio recognition method, comprising: identifying as key points in a spectrogram of a to-be-recognized audio file, points that have energy values exceeding a threshold value; if the energy value of a key point is a maximum value in a preset region, determining the key point as a first feature point; normalizing energy values of first feature points in the spectrogram of a to-be-recognized audio file into a range of grayscale values comprising: traversing frames of the spectrogram of the to-be-recognized audio file using a sliding window having a first preset length; acquiring a local maximum value and a local minimum value among the energy values of the first feature points within the window; normalizing, according to the local maximum value and the local minimum value, the energy values of the first feature points into the range of grayscale values; performing diffusion processing on the normalized first feature points in the spectrogram of the to-be-recognized audio file to obtain a feature point map, the diffusion processing comprising determining diffused feature points in the feature point map as respective regions around the normalized first feature points; searching in a spectrogram of a target audio file to determine whether second feature points that respectively correspond to said regions in the feature point map exist in the spectrogram of the target sound; and if yes, determining that the to-be-recognized audio file is a part of the target audio file. identifying as key points in a spectrogram of a to-be-recognized audio file, points that have energy values exceeding a threshold value; if the energy value of a key point is a maximum value in a preset region, determining the key point as a first feature point; normalizing energy values of first feature points in the spectrogram of a to-be-recognized audio file into a range of grayscale values comprising: traversing frames of the spectrogram of the to-be-recognized audio file using a sliding window having a first preset length; acquiring a local maximum value and a local minimum value among the energy values of the first feature points within the window; normalizing, according to the local maximum value and the local minimum value, the energy values of the first feature points into the range of grayscale values; traversing frames of the spectrogram of the to-be-recognized audio file using a sliding window having a first preset length; acquiring a local maximum value and a local minimum value among the energy values of the first feature points within the window; normalizing, according to the local maximum value and the local minimum value, the energy values of the first feature points into the range of grayscale values; performing diffusion processing on the normalized first feature points in the spectrogram of the to-be-recognized audio file to obtain a feature point map, the diffusion processing comprising determining diffused feature points in the feature point map as respective regions around the normalized first feature points; searching in a spectrogram of a target audio file to determine whether second feature points that respectively correspond to said regions in the feature point map exist in the spectrogram of the target sound; and if yes, determining that the to-be-recognized audio file is a part of the target audio file. <EOS>