Fine-grained　vehicle　recognition　plays　an　important　part　in　applications,　such　as　urban　traffic　management,　public　security,　and　criminal　investigation.　It　has　great　chanllengs　due　to　the　subtle　differences　among　numerous　subcategories.　In　this　paper,　a　fine-grained　vehicle　recognition　method　using　lightweight　convolutional　neural　network　with　combined　learning　strategy　is　proposed.　Firstly,　a　lightweight　Convolutional　Neural　Network　(LWCNN)　is　designed　specially　for　the　fine-grained　vehicle　recognition　task.　Then,　a　combined　training　strategy,　including　pre-training,　fine-tuning　training　and　transfer　training,　is　proposed　to　optimize　the　LWCNN　parameters.　In　the　pre-training　phase,　ILSVRC-2012　dataset　is　adopted　to　train　the　VGG16-Net,　generating　an　initial　model.　Then,　in　the　fine-tuning　phase,　the　vehicle　dataset　is　used　for　fine-tuning　the　pre-trained　model　to　avoid　learning　parameters　from　scratch.　Finally,　in　the　transfer　training　phase,　appropriate　initialization　parameters　of　LWCNN　are　obtained　through　the　analysis　of　the　fine-tuned　network　parameters.　LWCNN　is　then　trained　using　the　vehicle　dataset　to　obtain　the　highly　accurate　and　robust　classification　model.　Compared　with　the　state-of-the-art　methods,　the　proposed　method　can　effectively　decrease　the　computational　complexity　while　maintaining　the　recognition　performance.　©　2018　IEEE.