A voice recognition method, comprising: obtaining (S 100) voice data; obtaining a confidence value according to the voice data; obtaining a noise scenario according to the voice data, wherein the noise scenario comprises a noise type and a noise magnitude, the noise magnitude comprises a signal-to-noise ratio and a noise energy level, the noise type refers to a noise environment where a user is located when the voice data is input by the user, the noise magnitude represents a magnitude of noise in the noise environment where the user is located when the voice data is input by the user, the signal-to-noise ratio is a ratio of voice data power to noise data power, the noise energy level is used to reflect a magnitude of energy of noise data in the voice data of the user; obtaining a confidence threshold corresponding to the noise scenario; and processing the voice data, if the confidence value is greater than or equal to the confidence threshold. wherein the obtaining a noise scenario according to the voice data specifically comprises: obtaining, according to the voice data, a frequency cepstrum coefficient of a noise in the voice data; and obtaining, according to the frequency cepstrum coefficient of the noise and a pre-established noise type model, the noise type of the voice data; wherein the obtaining a noise scenario according to the voice data specifically comprises: obtaining, according to the voice data, a feature parameter of the voice data; performing voice activity detection according to the feature parameter; and obtaining the noise magnitude according to a result of the voice activity detection. obtaining (S 100) voice data; obtaining a confidence value according to the voice data; obtaining a noise scenario according to the voice data, wherein the noise scenario comprises a noise type and a noise magnitude, the noise magnitude comprises a signal-to-noise ratio and a noise energy level, the noise type refers to a noise environment where a user is located when the voice data is input by the user, the noise magnitude represents a magnitude of noise in the noise environment where the user is located when the voice data is input by the user, the signal-to-noise ratio is a ratio of voice data power to noise data power, the noise energy level is used to reflect a magnitude of energy of noise data in the voice data of the user; obtaining a confidence threshold corresponding to the noise scenario; and processing the voice data, if the confidence value is greater than or equal to the confidence threshold. wherein the obtaining a noise scenario according to the voice data specifically comprises: obtaining, according to the voice data, a frequency cepstrum coefficient of a noise in the voice data; and obtaining, according to the frequency cepstrum coefficient of the noise and a pre-established noise type model, the noise type of the voice data; wherein the obtaining a noise scenario according to the voice data specifically comprises: obtaining, according to the voice data, a feature parameter of the voice data; performing voice activity detection according to the feature parameter; and obtaining the noise magnitude according to a result of the voice activity detection. obtaining, according to the voice data, a frequency cepstrum coefficient of a noise in the voice data; and obtaining, according to the frequency cepstrum coefficient of the noise and a pre-established noise type model, the noise type of the voice data; wherein the obtaining a noise scenario according to the voice data specifically comprises: obtaining, according to the voice data, a feature parameter of the voice data; performing voice activity detection according to the feature parameter; and obtaining the noise magnitude according to a result of the voice activity detection. obtaining, according to the voice data, a feature parameter of the voice data; performing voice activity detection according to the feature parameter; and obtaining the noise magnitude according to a result of the voice activity detection.