This　article　proposes　a　novel　lightweight　attention　spatiotemporal　joint　distribution　adaptation　network　fault　diagnosis　model　to　address　the　key　challenges　of　domain　transfer　and　high　model　complexity　in　traditional　methods.　The　novelty　lies　in　1.　Using　model　compression　techniques　to　reduce　the　complexity　of　the　network　model　and　improve　its　computational　efficiency;　2.　Introducing　new　domain　adaptation　and　adversarial　methods　to　solve　the　domain　transfer　problem.　The　effectiveness　of　the　proposed　model　is　verified　through　a　transfer　experiment　of　planetary　gearbox　vibration　data.　The　experimental　results　show　that　the　proposed　model　reduces　the　parameters　and　computational　complexity　to　18　%　and　15　%　of　the　original　model,　respectively,　and　has　a　diagnostic　accuracy　of　over　98　%　in　cross-condition　transfer　tasks,　and　still　maintains　an　accuracy　of　over　88　%　even　under　high　noise　levels.　This　indicates　that　the　proposed　model　is　an　efficient　and　accurate　fault　diagnosis　model.