Abstract: The paraffin deposition on pumping well is a gradual process, and the changes in timing indicator diagram can reflect the degree of paraffin deposition in oil wells. Normally, it is usually to predict paraffin deposition degree and to determine thermal washing system for paraffin removal by using on-site experience, with low decision-making ability and poor paraffin removal effect. With the help of artificial intelligence technology, the correlation relationship between paraffin deposition degree and production parameters such as indicator diagram of pumping wells, motor operating parameters, and wellhead production parameters can be understood. Then, the researches on predicting, warning paraffin deposition and on evaluating thermal washing effect for pumping wells were performed driven by data. Extract the features of indicator diagram for paraffin deposition wells by using the residual convolutional neural network (ResNet), determine the paraffin deposition level by using the clustering algorithm, establish a sample set by combining the graphic features of the extracted indicator diagram with the 12 production parameters, train the sample set with a network structure model that constructed from sequence to sequence with long and short-term memory neural network (LSTM), establish a paraffin deposition level prediction model, quantitatively predict the paraffin deposition level for pumping wells, and construct an index Q evaluating paraffin removal effect for oil wells. The research results show that the established model predicting paraffin deposition and the constructed index evaluating paraffin removal effect for paraffin deposition wells have achieved quantitative prediction of paraffin precipitation level, decision-making of well cleaning cycle, and effective evaluation of paraffin removal effect. There is a perfect role for the established models to accurately guide paraffin removal timing and to guide paraffin removal evaluation, which may effectively avoid the paraffin stuck in the well and prolonging the no-clean cycle for oil wells.