Timely and accurate monitoring of the working conditions of oil wells is of great significance to the safe and efficient production of oilfields and the enhanced oil recovery. With the continuous deepening of oilfield informatization construction，real-time collection of dynamic monitoring data regarding oil well production such as indicator diagrams has been realized，and massive amounts of data have been accumulated and urgently need to be further explored and utilized. A new generation of artificial intelligence technology based on“big data+deep learning”is expected to break through the limitations of existing technologies and lead the upgrade of working condition diagnosis technology for oil wells. To this end，first，relying on more than 40 million sets of historical dynamic monitoring data covering oil wells in the different reservoirs，we prepared a large-scale dataset for working condition diagnosis of oil wells，which covered 5 categories and 37 different types of working conditions. On this basis，we selected the convolutional neural network algorithm and designed a personalized convolutional neural network（OWDNet）for working condition diagnosis of oil wells which contained more than 59 million learnable parameters in 26 layers. The OWDNet was trained using the above-mentioned working condition diagnosis dataset. After 10 epochs，the training accuracy was up to 99.7%，and the verification accuracy reached 98.9%. Furthermore，an intelligent working condition diagnosis system for oil wells was developed，and more than 5 million working condition diagnoses have been completed on site.The application accuracy of working condition diagnosis is 90%，and timely alarms are achieved. With this system，oil well production management and control were more reasonable and efficient，and working conditions of oil wells continued to improve. The proportion of continuous and stable production wells increased from 68% to 88%. The research provided a useful demonstration for the high-value application of oilfield big data.