A voice recognition method, comprising: acquiring first voice data; adjusting, according to the first voice data, a collection state of second voice data to obtain an adjusted collection state, and acquiring the second voice data based on the adjusted collection state; performing far-field voice recognition on the second voice data using a preset far-field voice recognition model so as to obtain semantic information corresponding to the second voice data; wherein the first voice data comprises a voice wake-up word, the voice wake-up word is voice data for waking up a voice device; and the second voice data comprises a voice command, the voice command is voice data for controlling the voice device; the adjusting, according to the first voice data, a collection state of second voice data, comprises: after determining location information of a sound source sending the first voice data, performing at least one of the following: enhancing an intensity of the voice collection device that collects the first voice data to collect the second voice data on the location information; and suppressing an intensity of the voice collection device that collects the first voice data to collect the second voice data at locations except the location information; the performing far-field voice recognition on the second voice data using a preset far-field voice recognition model comprises: preprocessing the second voice data to obtain voice information; performing, using the preset far-field voice recognition model, far-field voice recognition on the voice information obtained by preprocessing; the far-field voice recognition model comprises: a far-field acoustic model obtained by deep learning training based on an LSTM algorithm. acquiring first voice data; adjusting, according to the first voice data, a collection state of second voice data to obtain an adjusted collection state, and acquiring the second voice data based on the adjusted collection state; performing far-field voice recognition on the second voice data using a preset far-field voice recognition model so as to obtain semantic information corresponding to the second voice data; wherein the first voice data comprises a voice wake-up word, the voice wake-up word is voice data for waking up a voice device; and the second voice data comprises a voice command, the voice command is voice data for controlling the voice device; the adjusting, according to the first voice data, a collection state of second voice data, comprises: after determining location information of a sound source sending the first voice data, performing at least one of the following: enhancing an intensity of the voice collection device that collects the first voice data to collect the second voice data on the location information; and suppressing an intensity of the voice collection device that collects the first voice data to collect the second voice data at locations except the location information; the performing far-field voice recognition on the second voice data using a preset far-field voice recognition model comprises: preprocessing the second voice data to obtain voice information; performing, using the preset far-field voice recognition model, far-field voice recognition on the voice information obtained by preprocessing; the far-field voice recognition model comprises: a far-field acoustic model obtained by deep learning training based on an LSTM algorithm. enhancing an intensity of the voice collection device that collects the first voice data to collect the second voice data on the location information; and suppressing an intensity of the voice collection device that collects the first voice data to collect the second voice data at locations except the location information; the performing far-field voice recognition on the second voice data using a preset far-field voice recognition model comprises: preprocessing the second voice data to obtain voice information; performing, using the preset far-field voice recognition model, far-field voice recognition on the voice information obtained by preprocessing; the far-field voice recognition model comprises: a far-field acoustic model obtained by deep learning training based on an LSTM algorithm. preprocessing the second voice data to obtain voice information; performing, using the preset far-field voice recognition model, far-field voice recognition on the voice information obtained by preprocessing; the far-field voice recognition model comprises: a far-field acoustic model obtained by deep learning training based on an LSTM algorithm.