A method for adjusting a wireless modem, the method comprising: acquiring a channel parameter of the wireless modem at a present moment; generating , according to the channel parameter, a target clock frequency and a target working voltage of the wireless modem by using a neural network which is pre-trained; and adjusting a working voltage and a clock frequency of the wireless modem to the target working voltage and the target clock frequency respectively, characterized in that , the neural network is pre-trained through steps of: acquiring a sample input set and an associated sample output set, each sample input of the sample input set comprising a group of channel parameters which are preset for the wireless modem, each sample output of the associated sample output set comprising a sample working voltage and a sample clock frequency of the wireless modem which operates under a corresponding group of channel parameters, and the sample working voltage and the sample clock frequency being a target working voltage and a target clock frequency of the wireless modem when a preset performance condition is met; and training the neural network by using the group of channel parameters comprised in each sample input as the input of the neural network and by using the sample working voltage and the sample clock frequency comprised in the sample output associated with each sample input as output of the neural network. acquiring a channel parameter of the wireless modem at a present moment; generating , according to the channel parameter, a target clock frequency and a target working voltage of the wireless modem by using a neural network which is pre-trained; and adjusting a working voltage and a clock frequency of the wireless modem to the target working voltage and the target clock frequency respectively, characterized in that , the neural network is pre-trained through steps of: acquiring a sample input set and an associated sample output set, each sample input of the sample input set comprising a group of channel parameters which are preset for the wireless modem, each sample output of the associated sample output set comprising a sample working voltage and a sample clock frequency of the wireless modem which operates under a corresponding group of channel parameters, and the sample working voltage and the sample clock frequency being a target working voltage and a target clock frequency of the wireless modem when a preset performance condition is met; and training the neural network by using the group of channel parameters comprised in each sample input as the input of the neural network and by using the sample working voltage and the sample clock frequency comprised in the sample output associated with each sample input as output of the neural network. acquiring a sample input set and an associated sample output set, each sample input of the sample input set comprising a group of channel parameters which are preset for the wireless modem, each sample output of the associated sample output set comprising a sample working voltage and a sample clock frequency of the wireless modem which operates under a corresponding group of channel parameters, and the sample working voltage and the sample clock frequency being a target working voltage and a target clock frequency of the wireless modem when a preset performance condition is met; and training the neural network by using the group of channel parameters comprised in each sample input as the input of the neural network and by using the sample working voltage and the sample clock frequency comprised in the sample output associated with each sample input as output of the neural network. <EOS>