Gravitationally strongly lensed quasars (SL-QSO) offer invaluable insights into cosmological and astrophysical phenomena. With the data from ongoing and next-generation surveys, thousands of SL-QSO systems can be discovered expectedly, leading to unprecedented opportunities. However, the challenge lies in identifying SL-QSO from enormous data sets with high recall and purity in an automated and efficient manner. Hence, we developed a program based on a convolutional neural network (CNN) for finding SL-QSO from large-scale surveys and applied it to the Kilo-degree Survey Data Release 5. Our approach involves three key stages: first, we preselected 10 million bright objects (with r -band MAG_AUTO