Quickly　and　accurately　tracing　neuronal　morphologies　in　large-scale　volumetric　microscopy　data　is　a　very　challenging　task.　Most　automatic　algorithms　for　tracing　multi-neuron　in　a　whole　brain　are　designed　under　the　Ultra-Tracer　framework,　which　begins　the　tracing　of　a　neuron　from　its　soma　and　traces　all　signals　via　a　block-by-block　strategy.　Some　neuron　image　blocks　are　easy　for　tracing　and　their　automatic　reconstructions　are　very　accurate,　and　some　others　are　difficult　and　their　automatic　reconstructions　are　inaccurate　or　incomplete.　The　former　are　called　low　Tracing　Difficulty　Blocks　(low-TDBs),　while　the　latter　are　called　high　Tracing　Difficulty　Blocks　(high-TDBs).　We　design　a　model　named　3D-SSM　to　classify　the　tracing　difficulty　of　3D　neuron　image　blocks,　which　is　based　on　3D　Residual　neural　Network　(3D-ResNet),　Fully　Connected　Neural　Network　(FCNN)　and　Long　Short-Term　Memory　network　(LSTM).　3D-SSM　contains　three　modules:　Structure　Feature　Extraction　(SFE),　Sequence　Information　Extraction　(SIE)　and　Model　Fusion　(MF).　SFE　utilizes　a　3D-ResNet　and　a　FCNN　to　extract　two　kinds　of　features　in　3D　image　blocks　and　their　corresponding　automatic　reconstruction　blocks.　SIE　uses　two　LSTMs　to　learn　sequence　information　hidden　in　3D　image　blocks.　MF　adopts　a　concatenation　operation　and　a　FCNN　to　combine　outputs　from　SIE.　3D-SSM　can　be　used　as　a　stop　condition　of　an　automatic　tracing　algorithm　in　the　Ultra-Tracer　framework.　With　its　help,　neuronal　signals　in　low-TDBs　can　be　traced　by　the　automatic　algorithm　and　in　high-TDBs　may　be　reconstructed　by　annotators.　12732　training　samples　and　5342　test　samples　are　constructed　on　neuron　images　of　a　whole　mouse　brain.　The　3D-SSM　achieves　classification　accuracy　rates　87.04%　on　the　training　set　and　84.07%　on　the　test　set.　Furthermore,　the　trained　3D-SSM　is　tested　on　samples　from　another　whole　mouse　brain　and　its　accuracy　rate　is　83.21%.　©　2021,　The　Author(s).