Recently,　the　diagnosis　of　rotating　machines　based　on　deep　learning　models　has　achieved　great　success.　Many　of　these　intelligent　diagnosis　models　are　assumed　that　training　and　test　data　are　subject　to　independent　identical　distributions　(IIDs).　Unfortunately,　such　an　assumption　is　generally　invalid　in　practical　applications　due　to　noise　disturbances　and　changes　in　workload.　To　address　the　above　problem,　this　article　presents　a　high-stability　diagnosis　model　named　the　multiscale　feature　fusion　convolutional　neural　network　(MFF-CNN).　MFF-CNN　does　not　rely　on　tedious　data　preprocessing　and　target　domain　information.　It　is　composed　of　multiscale　dilated　convolution,　self-adaptive　weighting,　and　the　new　form　of　maxout　(NFM)　activation.　It　extracts,　modulates,　and　fuses　the　input　samples＇　multiscale　features　so　that　the　model　focuses　more　on　the　health　state　difference　rather　than　the　noise　disturbance　and　workload　difference.　Two　diagnostic　cases,　including　noisy　cases　and　variable　load　cases,　are　used　to　verify　the　effectiveness　of　the　present　model.　The　results　show　that　the　present　model　has　a　strong　health　state　identification　capability　and　anti-interference　capability　for　variable　loads　and　noise　disturbances.