Convolution　neural　network　(CNN)-based　transfer　learning　(TL)　has　been　investigated　for　knowledge　transfer　in　machinery　fault　diagnosis.　However,　there　remain　certain　challenges　that　need　to　be　addressed:　firstly,　the　optimization　of　deep　network　models　is　hindered　by　the　substantial　number　of　parameters　involved;　and　secondly,　the　limitation　of　training　data　impacts　the　diagnostic　accuracy　of　the　model.　In　this　work,　a　multi-level　residual　CNN　based　on　dynamic　feature　fusion　(MRCNN-DFF)　is　proposed　for　TL　in　machinery　fault　diagnosis.　In　MRCNN-DFF,　depth-wise　separable　(DS)　convolution　is　introduced　to　reduce　the　number　of　trainable　parameters,　which　offers　the　advantages　and　enhances　the　efficiency　of　model　optimization.　Meanwhile,　to　fully　mine　the　useful　information,　parallel　MRCNN　channels　are　proposed　to　extract　high-dimensional　features.　Then,　the　DFF　is　designed　to　fuse　the　bi-channel　information　according　to　the　feature　importance,　which　effectively　incorporates　the　key　information　contained　in　the　limited　data.　Finally,　the　parameter　transfer　is　utilized　to　transfer　domain-shared　information.　The　MRCNN-DFF　is　validated　employing　two　bearing　datasets,　and　the　results　demonstrate　that　the　proposed　MRCNN-DFF　surpasses　the　comparison　methods　in　terms　of　performance.　　©　2024　IEEE.