A method for constructing a speech decoding network in speech recognition of digits, comprising: acquiring training data obtained by speech recording of digits, wherein the training data comprises a plurality of speech segments, and each speech segment comprises voice of a plurality of digits; performing acoustic feature extraction on the training data to obtain a feature sequence corresponding to each speech segment; performing progressive training starting from a mono-phoneme acoustic model to obtain an acoustic model according to the feature sequences and phonemes corresponding to digits in the training data; acquiring a language model, and constructing a speech decoding network by using the language model and the acoustic model obtained by training; wherein the performing progressive training starting from a mono-phoneme acoustic model to obtain an acoustic model according to the feature sequences and phonemes corresponding to digits in the training data comprises: acquiring a mono-phoneme corresponding to digits from the speech segments of the training data; performing acoustic modeling and training on the feature sequence corresponding to the speech segment according to the divided states of the mono-phoneme to obtain a mono-phoneme acoustic model corresponding to the mono-phoneme; recognizing the training data by using the mono-phoneme acoustic model to obtain secondary training data; obtaining a corresponding tri-phoneme according to the digits contained in the speech segments in the secondary training data; performing acoustic modeling and training on the feature sequence corresponding to the speech segment according to the divided states of the tri-phoneme to obtain an acoustic model corresponding to the tri-phoneme. acquiring training data obtained by speech recording of digits, wherein the training data comprises a plurality of speech segments, and each speech segment comprises voice of a plurality of digits; performing acoustic feature extraction on the training data to obtain a feature sequence corresponding to each speech segment; performing progressive training starting from a mono-phoneme acoustic model to obtain an acoustic model according to the feature sequences and phonemes corresponding to digits in the training data; acquiring a language model, and constructing a speech decoding network by using the language model and the acoustic model obtained by training; wherein the performing progressive training starting from a mono-phoneme acoustic model to obtain an acoustic model according to the feature sequences and phonemes corresponding to digits in the training data comprises: acquiring a mono-phoneme corresponding to digits from the speech segments of the training data; performing acoustic modeling and training on the feature sequence corresponding to the speech segment according to the divided states of the mono-phoneme to obtain a mono-phoneme acoustic model corresponding to the mono-phoneme; recognizing the training data by using the mono-phoneme acoustic model to obtain secondary training data; obtaining a corresponding tri-phoneme according to the digits contained in the speech segments in the secondary training data; performing acoustic modeling and training on the feature sequence corresponding to the speech segment according to the divided states of the tri-phoneme to obtain an acoustic model corresponding to the tri-phoneme. acquiring a mono-phoneme corresponding to digits from the speech segments of the training data; performing acoustic modeling and training on the feature sequence corresponding to the speech segment according to the divided states of the mono-phoneme to obtain a mono-phoneme acoustic model corresponding to the mono-phoneme; recognizing the training data by using the mono-phoneme acoustic model to obtain secondary training data; obtaining a corresponding tri-phoneme according to the digits contained in the speech segments in the secondary training data; performing acoustic modeling and training on the feature sequence corresponding to the speech segment according to the divided states of the tri-phoneme to obtain an acoustic model corresponding to the tri-phoneme.