A method for constructing a voice detection model, comprising: collecting audio data first and synthesizing a mixed voice, performing feature extraction on the mixed voice to obtain a 62-dimensional feature, and then inputting the 62-dimensional feature to a recurrent neural network, RNN, model for training to obtain a voice detection model; wherein the method specifically comprising the following steps: Step 1, Mixed voice synthesis: collecting a pure voice and noise first, and then synthesizing the collected pure voice and noise to obtain the mixed voice; Step 2, Feature extraction: performing windowing and framing on the mixed voice first and performing short-time Fourier transform, calculating a frequency domain log spectrum of each frame, and then performing feature extraction on the log spectrum to obtain the 62-dimensional feature; and Step 3, Generation of the voice detection model: inputting the 62-dimensional feature to the RNN model, and then using an Adam training strategy and an improved loss function for training to obtain the voice detection model, wherein the improved loss function is as follows: BCE min x i = 0.1 × MC x × − y i logf i x + 1 − y i log 1 − f i x, wherein BCE min (x) i represents a binary cross entropy, f i ( x) represents a predicted result, y i represents a true label, i represents an index of data and MC ( x) represents a minimum expected cost whose formula is as follows: MC x = P pre L 0, true L 1, x × C 10 + P pre L 1, true L 0, x × C 0, wherein in the formula, x represents an input value of the RNN model, namely, the 62-dimensional feature; pre represents a predicted result; true represents a true label; P(pre L0, true L1, x) represents a ratio of a number of true labels L1, predicted as L0, to a total number, L1 being a voice label, and L0 being a non-voice label; P(pre L1, true L0, x) represents a ratio of a number of true labels L0, predicted as L1, to the total number; C10 is a cost when a true label L1 is detected as L0; and C0 is a cost when a true label L0 is detected as L1; wherein the RNN model comprises a Dense layer of 16 neurons and a gated recurrent unit, GRU, layer of 24 outputs. Step 1, Mixed voice synthesis: collecting a pure voice and noise first, and then synthesizing the collected pure voice and noise to obtain the mixed voice; Step 2, Feature extraction: performing windowing and framing on the mixed voice first and performing short-time Fourier transform, calculating a frequency domain log spectrum of each frame, and then performing feature extraction on the log spectrum to obtain the 62-dimensional feature; and Step 3, Generation of the voice detection model: inputting the 62-dimensional feature to the RNN model, and then using an Adam training strategy and an improved loss function for training to obtain the voice detection model, wherein the improved loss function is as follows: BCE min x i = 0.1 × MC x × − y i logf i x + 1 − y i log 1 − f i x, wherein BCE min (x) i represents a binary cross entropy, f i ( x) represents a predicted result, y i represents a true label, i represents an index of data and MC ( x) represents a minimum expected cost whose formula is as follows: MC x = P pre L 0, true L 1, x × C 10 + P pre L 1, true L 0, x × C 0, wherein in the formula, x represents an input value of the RNN model, namely, the 62-dimensional feature; pre represents a predicted result; true represents a true label; P(pre L0, true L1, x) represents a ratio of a number of true labels L1, predicted as L0, to a total number, L1 being a voice label, and L0 being a non-voice label; P(pre L1, true L0, x) represents a ratio of a number of true labels L0, predicted as L1, to the total number; C10 is a cost when a true label L1 is detected as L0; and C0 is a cost when a true label L0 is detected as L1; wherein the RNN model comprises a Dense layer of 16 neurons and a gated recurrent unit, GRU, layer of 24 outputs.