In　multi-person　videos,　especially　team　sport　videos,　a　semantic　event　is　usually　represented　as　a　confrontation　between　two　teams　of　players,　which　can　be　represented　as　collective　motion.　In　broadcast　basketball　videos,　specific　camera　motions　are　used　to　present　specific　events.　Therefore,　a　semantic　event　in　broadcast　basketball　videos　is　closely　related　to　both　the　global　motion　(camera　motion)　and　the　collective　motion.　A　semantic　event　in　basketball　videos　can　be　generally　divided　into　three　stages:　pre-event,　event　occurrence　(event-occ),　and　post-event.　By　analyzing　the　influence　of　different　stages　of　video　segments　to　semantic　events　discrimination,　it　is　observed　that　the　pre-event　and　event-occ　segments　are　effective　for　classification,　while　the　post-events　are　effective　for　event　success/failure　classification.　In　this　paper,　we　propose　an　ontology-based　global　and　collective　motion　pattern　(On_GCMP)　algorithm　for　the　basketball　event　classification.　First,　a　two-stage　GCMP-based　event　classification　scheme　is　proposed.　The　GCMP　is　extracted　using　the　optical　flow.　The　two-stage　scheme　progressively　combines　a　five-class　event　classification　algorithm　on　event-occs　and　a　two-class　event　classification　algorithm　on　pre-events.　Both　algorithms　utilize　the　sequential　convolutional　neural　networks　(CNNs)　and　the　long　short-term　memory　(LSTM)　networks　to　extract　the　spatial　and　temporal　features　of　GCMP　for　event　classification.　Second,　we　utilize　the　post-event　segments　to　predict　success/failure　using　deep　features　of　images　in　the　video　frames　(RGB_DF_VF)-based　algorithms.　Finally,　the　event　classification　results　and　success/failure　classification　results　are　integrated　to　obtain　the　final　results.　To　evaluate　the　proposed　scheme,　we　collected　a　new　dataset　called　NCAA+,　which　is　automatically　obtained　from　the　NCAA　dataset　by　extending　the　fixed　length　of　video　clips　forward　and　backward　of　the　corresponding　semantic　events.　The　experimental　results　demonstrate　that　the　proposed　scheme　achieves　the　mean　average　precision　of　58.10%　on　NCAA+.　It　is　higher　by　6.50%　than　the　state　of　the　art　on　NCAA.