With the concept of energy conservation and environmental protection and the concept of sustainable green development deeply rooted in the hearts of people, how to do a good job in energy conservation and emission reduction of blast furnaces has become one of the main problems facing the steel industry at present. In order to achieve energy conservation and emission reduction of blast furnace, a multi-objective optimization scheme with the lowest fuel ratio and the highest coal ratio is proposed by combining artificial intelligence technology with blast furnace production data. In terms of fuel ratio and coal ratio prediction, random forest (RF), long short memory network structure (LSTM), and long short memory combined with attention mechanism (LSTM Attention) are used for comparative analysis, and LSTM Attention model, which is the most accurate for fuel ratio and coal ratio prediction, is selected as the prediction model. On the basis of LSTM Attention prediction model, combined with multi-objective particle swarm optimization (MOPSO) and non dominated sorting genetic algorithm (NSGA-II), the pareto optimal solution is found and compared, and MOPSO with better effect is selected for result analysis. The results show that under a certain production condition of the blast furnace, the energy consumption can be reduced by 4.06% by controlling the parameter values of the decision variables such as pressure difference, oxygen content, coal injection volume and air volume×1011 kJ/year, reducing CO2 emissions by 25.91 t/year, providing technical support for energy conservation and emission reduction of blast furnace.