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When there are interference factors such as noise in the communication channel, the performance of quadrature phase shift keying receiver in demodulating received signals is poor.In view of the problem,this paper studied a QPSK intelligent receiver model based on deep learning.The QPSK intelligent receiver model is composed of long and short-term memory neural network and fully connected layer. With the help of the memory structure in the recurrent neural network, it also uses the characteristic of LSTM can extract the temporal correlation of the received signal. Simulation experiments with a signal to noise ratio at 0 to 7 dB showed that,under the influence of Gaussian additive white noise,inphase and quadrature imbalance and frequency deviation interference factors, the bit error rate of the proposed QPSK intelligent receiver model at 0 to 7 dB is significantly reduced compared with that of the communication receiver using the traditional hard decision method.Among them, the bit error rate of QPSK intelligent receiver model at 7dB is as low as 0.0109%, which is only about 1/7 of the bit error rate of the traditional hard decision method. In the event of frequency deviation and IQ imbalance, the bit error rate of QPSK intelligent receiver model at 7dB is as low as 0.0147% and 0.0198%, respectively, which are much lower than the bit error rate of the traditional hard decision method under the same condition. Therefore, adopting the QPSK intelligent receiver model proposed in this paper can significantly improve the detection performance of the receiver.