Signed networks with node attributes are widely used in many fields such as informatics and biology. Link sign prediction is ahot topic in this kind of data analysis.?The model based on signed graph neural networks is the latest effective solution to this problem,but the existing methods are almost based on social balance theory,and do not make full use of node attributes. Aiming at the aboveproblems,a signed graph neural network was designed from the view of graph signal processing and an end-to-end link relationship prediction algorithm for attributed and signed graphs was proposed based on graph filter. Firstly,based on low frequency and high frequencysignals,a signed graph neural network with band-pass filter was proposed to obtain node embedding based on signed topology graph.Secondly,an attributed similarity graph was constructed and node representation was obtained by graph convolution networks. Finally,theattention mechanism was introduced to fuse two kinds of node representation of signed topology graph and attributed similarity graph. Itsinput was used to train symbol discriminator,and the model was trained by Adam optimizer. Experiments are conducted on three drugdatasets,in order to analyze the comparison to baselines and the effect of model settings. Compared with typical models such as signedgraph convolution network and signed spectral embedding models,and the newly proposed signed graph filtering-based convolutional network,the AUC and F1 metric values are superior to the best baseline method up to 8. 68% and 10. 04% ,respectively.