Social　trust　assessment　that　characterizes　a　pairwise　trustworthiness　relationship　can　spur　diversified　applications.　Extensive　efforts　have　been　put　in　exploration,　but　mainly　focusing　on　applying　graph　convolutional　network　to　establish　a　social　trust　evaluation　model,　overlooking　user　feature　factors　related　to　context-aware　information　on　social　trust　prediction.　In　this　article,　we　aim　to　design　a　new　trust　assessment　framework　GATrust　which　integrates　multi-aspect　properties　of　users,　including　user　context-specific　information,　network　topological　structure　information,　and　locally-generated　social　trust　relationships.　GATrust　can　assigns　different　attention　coefficients　to　multi-aspect　properties　of　users　in　online　social　networks,　for　improving　the　prediction　accuracy　of　social　trust　evaluation.　The　framework　can　then　learn　multiple　latent　factors　of　each　trustor-trustee　pair　to　establish　a　social　trust　evaluation　model,　by　fusing　graph　attention　network　and　graph　convolution　network.　We　conduct　extensive　experiments　on　two　popular　real-world　datasets　and　the　results　exhibit　that　our　proposed　framework　can　improve　the　precision　of　social　trust　prediction,　outperforming　the　state-of-the-art　in　the　literature　by　4.3%　and　5.5%　on　both　two　datasets,　respectively.