To solve the issue of low accuracy of the traditional soft sensor methods of polypropylene melt index

an approach based on deep belief network and extreme learning machine（DBN-ELM）was used to the melt index prediction of polypropylene. Traditional deep belief network(DBN) applied the deep learning to the learning process of the deep neural networks. Different from traditional deep belief network

this approach applied the extreme learning machine algorithm(ELM) to the learning process of DBN to improve the DBN model. Firstly

deep belief network was employed to extract effective features from vibration data by numerical analysis. Then

the effective features were put into the extreme learning machine to proceed model training to obtain the soft sensor model. The experimental validation showed that the method was more accuracy than the traditional method.