Dynamic　human　activity　intensity　information　is　of　great　importance　in　many　location-based　applications.　However,　two　limitations　remain　in　the　prediction　of　human　activity　intensity.　First,　it　is　hard　to　learn　the　spatial　interaction　patterns　across　scales　for　predicting　human　activities.　Second,　social　interaction　can　help　model　the　activity　intensity　variation　but　is　rarely　considered　in　the　existing　literature.　To　mitigate　these　limitations,　we　proposed　a　novel　dynamic　activity　intensity　prediction　method　with　deep　learning　on　graphs　using　the　interactions　in　both　physical　and　social　spaces.　In　this　method,　the　physical　interactions　and　social　interactions　between　spatial　units　were　integrated　into　a　fused　graph　convolutional　network　to　model　multi-type　spatial　interaction　patterns.　The　future　activity　intensity　variation　was　predicted　by　combining　the　spatial　interaction　pattern　and　the　temporal　pattern　of　activity　intensity　series.　The　method　was　verified　with　a　country-scale　anonymized　mobile　phone　dataset.　The　results　demonstrated　that　our　proposed　deep　learning　method　with　combining　graph　convolutional　networks　and　recurrent　neural　networks　outperformed　other　baseline　approaches.　This　method　enables　dynamic　human　activity　intensity　prediction　from　a　more　spatially　and　socially　integrated　perspective,　which　helps　improve　the　performance　of　modeling　human　dynamics.