Intelligent　controllers　based　on　the　broad　learning　system　can　simplify　the　process　of　model　parameter　adjustment,　finding　wide　applications　in　the　motion　control　of　multi-joint　robotic　arms.　However,　motion　controllers　for　multi-joint　robotic　arms　based　on　broad　learning　system　exhibit　insufficient　precision　and　overlook　the　impact　of　joint　motion　commonalities　on　controller　design.　Therefore,　this　paper　proposes　a　novel　motion　control　strategy　for　a　multi-joint　robotic　arm　based　on　a　deep　cascade　feature-enhancement　gated　Bayesian　broad　learning　system.　Firstly,　the　motion　controller　of　the　deep　cascade　feature-enhancement　Bayesian　broad　learning　system　is　constructed　to　enhance　the　robotic　arm　motion　control　precision.　Secondly,　an　incremental　node　generation　module　with　an　attention-gated　mechanism　is　constructed　to　capture　the　unique　motion　characteristics　of　the　target　joints,　which　is　further　combined　with　model　generalization　to　simplify　the　motion　control　process　of　the　multi-joint　robotic　arm.　Finally,　controller　convergence　is　enhanced　by　combining　it　with　the　Lyapunov　theory　to　constrain　the　learning　parameters.　Simulations　and　physical　experiments　are　designed　to　verify　the　feasibility　and　superiority　of　the　proposed　motion　control　strategy.　The　results　demonstrated　that　the　strategy　improved　the　accuracy　of　robotic　arm　motion　control,　with　the　root　mean　square　error　in　position　tracking　reduced　to　0.0019　rad.　This　represents　a　93.39%　reduction　in　error　compared　to　existing　techniques.