A data-driven model framework integrating Feature Subset Selection (FSS), production pattern clustering analysis and prediction was proposed for predicting ethylene yield of ethylene plant by using the massive sensing data recorded by the Distributed Control System (DCS) of petrochemical enterprises. Firstly, an Ensemble-Filter FSS model based on three different metrics is designed to initially filter all the steam cracking furnace features, and then a Wrapper FSS model based on GA-SVR is used to obtain the optimal subset of features affecting ethylene yield. The steam cracking furnace was identified based on the Density Peak Clustering (DPC) algorithm based on the production patterns embedded in the data. Ethylene yield prediction models were separately developed for each production pattern to summarize the final prediction results. The proposed model was validated against historical data from an industrial steam cracking furnace in northwest China. Results show that the number of features have a 93.4% reduction in the FSS stage. and a 40.6% reduction in predicted MSE. Compared with the benchmark ANN model, the proposed DPNN model has a 56.6% reduction in MSE based on the optimal cluster result. What’s more, the proposed framework has a strong generalization ability and with a modular structure which is easy to modify., which is expected to be used to guide the ethylene plant operating in reasonable intervals. •A novel data-driven model framework of steam cracking process is proposed.•The dataset used in this work has a large size coming from an industrial plant.•Feature number is reduced by 93.4% in feature subset selection stage.•Optimal feature subset is highly correlated with the process experience.•Modeling with production patterns identified data can improve the model performance.