In this paper

the background classification method of marine target detection based on convolutional neural network (CNN) is mainly studied. Taking LeNet as an example

based on the IPIX measured data set

the model training through controlling variables is carried out. The feasibility of using CNN in the classification of sea clutter and noise in one dimensional radar echo signal is studied

and the influence of factors such as data preprocessing

single sample sequence length and network structure parameters on classification accuracy is analyzed synchronously

and verified for the typical detection scene classification. The application results of measured data show that the proposed method has high accuracy in clutter classification and noise classification under the conditions of forward/reverse direction and high/low sea conditions.