By applying deep learning techniques, we explore the possibility of strange-quark tagging, which is the last missing piece among quark and gluon identifications in jets. The main difficulty here is of distinguishing strange-quark jets from down-quark jets. However, strange-quark jets are likely to contain more Kaons carrying large fractions of the jet $p_T$ than down-quark jets. A strategy for strange-quark tagging is then to concentrate on neutral Kaons, $K_L$ and $K_S$, which are expected to be discriminated from other hadrons as the $K_L$ and long-lived $K_S$ drop their energies only to the Hadron Calorimeter while other hadrons leave some trace in the tracker or the Electromagnetic Calorimeter. We create the pixel images of strange and down-quark jets with colors of the track $p_T$, hadronic energy and electromagnetic energy. The images are fed into Convolutional Neural Networks (CNNs). We find that the CNN tagger outperforms the best cut-based tagger we define.