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In order to realize the brain computer interaction between astronauts and space manipulators, aiming at the steady-state visual evoked potential brain-computer interface (SSVEP-BCI), the SSVEP signals classification method based on convolutional neural network is proposed. This method takes the characteristics of SSVEP signals after fast Fourier transform as the input, and realizes the classification and recognition of SSVEP signals through three-layer convolution and full connection. The benchmark data set of Tsinghua University is used to verify the method. Under the time window of 1 second, the average classification accuracy is 99.07% and the average information transmission rate is 149.24b/min, which are significantly higher than those using canonical correlation analysis or filter bank canonical correlation analysis. The comparison result shows that the proposed method has good target classification effect for short-time SSVEP signals. Finally, the recognized signals are used as the control signals to operate the space manipulator in the simulation environment to realize the brain computer interaction between human and space manipulators.