The　demand　for　agricultural　products　continues　to　increase　while　there　is　little　room　for　cropland　expansion.　Agricultural　intensification　on　existing　croplands　may　provide　a　promising　solution　to　increase　agricultural　production　and　alleviate　the　human-land　conflict.　To　achieve　this,　the　spatio-temporal　dynamics　of　cropping　intensity　are　essential　information　for　agricultural　production.　However,　existing　methods　are　usually　based　on　machine　learning　and　are　highly　reliant　on　training　samples.　There　are　relatively　few　studies　on　automatic　change　detection　of　cropping　intensity.　In　this　study,　a　knowledge-based　dynamic　Bayesian　network　model　for　change　analysis　(DBN-CA)　was　developed　for　automatic　detection　of　cropping　intensity　dynamics　as　well　as　the　overall　trends.　Four　statuses　of　cropping　intensity　dynamics　were　designed　by　referencing　crop　phenology　to　delineate　the　temporal　dynamics　of　cropping　intensity,　and　the　overall　trends　from　2001　to　2019　were　extracted　based　on　their　combinations.　The　conclusions　were　as　follows:　(1)　the　DBN-CA　model　based　on　crop　phenology　knowledge　has　a　remarkable　ability　to　effectively　detect　cropping　intensity　dynamics　as　well　as　its　overall　trends.　(2)　The　cropping　intensity　in　Hubei　Province　has　shown　a　significant　decreasing　trend　during　the　early　21st　century.