This　work　presents　the　deep　learning　networks-based　method　using　fine-tuning　for　classification　and　search　of　a　diversity　of　action　videos.　First,　a　3D　convolutional　neural　networks　(3D　CNN)　model　which　performs　pre-training　operation　and　fine-tuning　strategy　is　employed　to　extract　the　spatiotemporal　features　of　videos.　It　is　first　pre-trained　on　UCF-101　datasets　to　train　model　with　initial　parameters.　Then,　a　small　new　dataset　is　employed　to　fine-tune　the　initial　model　for　the　training　of　the　new　model.　Once　features　are　extracted　by　the　final　CNNs　model,　distance　measure　can　be　adopted　to　calculate　the　similarities　between　the　query　video　and　the　test　dataset　for　the　video　search.　The　searched　video　is　returned　and　ranked　according　to　the　priority　when　it　has　higher　similarity　with　the　query　video.　The　comparison　results　in　the　experiment　shows　that　the　search　method　using　fine-tuning　obtains　better　performance　than　the　method　without　using　fine-tuning.　Second,　the　classification　results　based　on　the　3D　CNN　model　using　fine-tuning　are　also　presented　for　the　consideration　of　a　query　by　keyword.　Accuracy　result　obtained　using　the　model　with　the　help　of　fine-tuning　is　approximately　2.8%　higher　than　that　without　using　fine-tuning.