In this paper, we study the problem of signal detection for massive multiple-input multiple-output (MIMO) communication systems aided by the intelligent reflecting surface (IRS). In the IRS-assisted massive MIMO systems, the signal between the base station and the users is reflected and hence the signal detection is a challenge problem. This paper proposes a model and data hybrid driven detection method to detect the signal of the receiver through neural network when the channel characteristics change. In the auxiliary transmission of IRS, the signal passes through two hop channels, one hop is the channel between transmitter and the IRS, the other hop is the channel between IRS and receiver. According to the different channel characteristics of two hop channel, a novel neural network detector which is combined the data-driven with model-driven is proposed. Experimental results show that the proposed detection scheme can achieve lower computational complexity and higher detection performance than traditional methods.