The　diagnostic　performance　of　deep　learning　models　is　heavily　reliant　on　large　volumes　of　labeled　training　data.　However,　in　practical　applications,　bearing　fault　samples　are　relatively　scarce,　and　the　availability　of　samples　for　effective　model　training　is　even　more　limited,　leading　to　the　suboptimal　performance　of　traditional　deep　learning　methods　in　bearing　fault　diagnosis.　To　address　the　issue　of　poor　performance　in　few-shot　bearing　fault　diagnosis,　a　novel　Adaptive　Deep　Siamese　Residual　Network　(ADSRN)　is　proposed　in　this　study.　Frequency-domain　information　is　extracted　using　the　Fourier　Transform,　and　training　samples　are　randomly　paired　according　to　the　matching　criteria　defined　by　the　Siamese　network　to　augment　the　dataset.　A　novel　Dynamic　Time　Warping　(DTW)　technique　is　applied　to　non-linearly　adjust　the　sequence　information,　allowing　for　the　precise　calculation　of　the　optimal　match　between　two　sequences　by　detecting　subtle　differences.　Additionally,　inspired　by　the　concept　of　dynamic　soft-hard　threshold　matching　in　unsupervised　learning,　an　innovative　strategy　for　dynamically　adjusting　the　adaptive　threshold　has　been　developed　to　enhance　the　generalization　capability　of　the　proposed　ADSRN.　Multiple　few-shot　fault　diagnosis　experiments　were　conducted　on　two　bearing　datasets　and　compared　with　several　state-of-the-art　methods.　Through　rigorous　experimental　evaluations,　the　effectiveness　and　superiority　of　the　proposed　ADSRN,　as　well　as　the　advantages　of　DTW,　were　validated.