Adaptive surface-related multiple subtraction is an important step of surface-related multiple elimination (SRME) method. Generally, the traditional methods match the predicted multiples with the original data using obtained filter. In this letter, we propose a matching algorithm based on convolutional neural network (CNN) to strike a balance between the attenuation of multiples and the protection of primaries. Taking the predicted surface-related multiples as input, CNN’s output can better match with the original data. From the processing results of synthetic data, compared with the traditional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> -norm or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula> -norm method, CNN method has lower calculation cost and the signal-to-noise ratio (SNR) of the primaries obtained after matching and subtraction is increased by 3 dB. The test of Pluto data and physical simulation data show that the proposed method can effectively remove surface-related multiples in seismic data.