With　the　rapid　development　of　the　Industrial　Internet　of　Things　(IIoT),　massive　IIoT　devices　connect　to　industrial　networks　via　wired　and　wireless.　Furthermore,　industrial　networks　pose　new　requirements　on　communications,　such　as　strict　latency　boundaries,　ultra-reliable　transmission,　and　so　on.　To　this　end,　time-sensitive　networking　(TSN)　embedded　fifth-generation　(5G)　wireless　communication　technology　(i.e.,　TSN-5G　networks),　is　considered　the　most　promising　solution　to　address　these　challenges.　TSN　can　provide　deterministic　end-to-end　latency　and　reliability　for　real-time　applications　in　wired　networks.　5G　supports　ultra-reliable　and　low-latency　communications　(uRLLC),　providing　increased　flexibility　and　inherent　mobility　support　in　the　wireless　network.　Thus,　the　integration　of　TSN　and　5G　provides　numerous　benefits,　including　increased　flexibility,　lower　commissioning　costs,　and　seamless　interoperability　of　various　devices,　regardless　of　whether　they　use　a　wired　or　wireless　interface.　Nonetheless,　the　potential　barriers　between　the　TSN　and　5G　systems,　such　as　clock　synchronization　and　end-to-end　traffic　scheduling,　are　inevitable.　Time　synchronization　has　been　studied　in　many　works,　so　this　paper　focuses　on　the　end-to-end　traffic　scheduling　problem　in　TSN-5G　networks.　We　propose　a　novel　integrated　TSN　and　5G　industrial　network　architecture,　where　the　5G　system　acts　as　a　logical　TSN-capable　bridge.　Based　on　this　network　architecture,　we　design　a　Double　Q-learning　based　hierarchical　particle　swarm　optimization　algorithm　(DQHPSO)　to　search　for　the　optimal　scheduling　solution.　The　DQHPSO　algorithm　adopts　a　level-based　population　structure　and　introduces　Double　Q-learning　to　adjust　the　number　of　levels　in　the　population,　which　evades　the　local　optimum　to　further　improve　the　search　efficiency.　Extensive　simulations　demonstrate　that　the　DQHPSO　algorithm　can　increase　the　scheduling　success　ratio　of　time-triggered　flows　compared　to　other　algorithms.