Automated　thyroid　nodule　classification　in　ultrasound　images　is　an　important　way　to　detect　thyroid　nodules　and　to　make　a　more　accurate　diagnosis.　In　this　paper,　we　propose　a　novel　deep　convolutional　neural　network　(CNN)　model,　called　n-ClsNet,　for　thyroid　nodule　classification.　Our　model　consists　of　a　multi-scale　classification　layer,　multiple　skip　blocks,　and　a　hybrid　atrous　convolution　(HAC)　block.　The　multi-scale　classification　layer　first　obtains　multi-scale　feature　maps　in　order　to　make　full　use　of　image　features.　After　that,　each　skip-block　propagates　information　at　different　scales　to　learn　multi-scale　features　for　image　classification.　Finally,　the　HAC　block　is　used　to　replace　the　downpooling　layer　so　that　the　spatial　information　can　be　fully　learned.　We　have　evaluated　our　n-ClsNet　model　on　the　TNUI-2021　dataset.　The　proposed　n-ClsNet　achieves　an　average　accuracy　(ACC)　score　of　93.8%　in　the　thyroid　nodule　classification　task,　which　outperforms　several　representative　state-of-the-art　classification　methods.