The　paper　proposes　an　unsupervised　deep　convolutional　dynamic　joint　distribution　domain　adaptive　network　model　for　the　problem　of　bearing　fault　diagnosis　under　variable　conditions,　which　involves　missing　labeling　of　target　domain　data　and　large　differences　in　the　distribution　of　source　and　target　domain　data.　The　model　consists　of　the　following　steps:　(1)　converting　the　original　vibration　signal　of　the　bearing　into　a　time-frequency　map　representation　and　performing　feature　extraction　on　the　labeled　source　domain　samples　and　the　unlabeled　target　domain　samples　by　the　deep　convolutional　feature　extractor;　(2)　dynamically　aligning　the　marginal　distribution　and　conditional　distribution　of　the　two　domain　features　by　the　marginal　distribution　adaptation　module　and　the　conditional　distribution　adaptation　module,　so　that　the　trained　network　model　can　classify　the　unlabeled　target　domain　samples　accurately　according　to　the　label　mapping　relationship　of　the　source　domain　samples;　(3)　validating　the　model　on　two　rolling　bearing　datasets;　(4)　experiment　with　model　interpretability　in　conjunction　with　XAI　techniques　to　help　us　understand　what　the　model　actually　does.　The　experimental　results　on　two　rolling　bearing　datasets　show　the　validity　of　the　proposed　model.