The traditional wireless communication system is composed of transmitters and receivers. The information to be transmitted is transmitted through antenna after channel coding， modulation， and shaping. Due to the influence of factors such as channel fading， noise， and interference， signals arriving at the receiver will have serious distortion， and the receiver needs to recover original information from distorted signals as much as possible. To solve this problem， a Multi-Input Multi-Output （MIMO） intelligent receiver model based on multi-label classification neural network was proposed. In this model， Deep Neural Network （DNN） was used to replace the entire information recovery link of receiver from signals to information， and multi-label classification algorithm was used to replace multiple binary classifiers to achieve multi-bit information flow recovery. The training dataset has quadrature signals that contains two modulation modes including Binary Phase Shift Keying （BPSK） and Quadrature Phase Shift Keying （QPSK） as well as two coding modes of Hamming coding and cyclic coding. Experimental results show that under conditions such as noise， Rayleigh fading， and interference， when the Bit Error Rate （BER） of receiver using the traditional Alamouti decoding method is 1E-3， the intelligent receiver realizes the recovered information with the BER of 0. While maintaining the same BER performance， the proposed multi-label classification algorithm reduces the training time of each batch by about 4 min compared with the multiple binary classifier algorithms of the comparison model.