© 2020 ACM. In the current era of digital media, people are greatly interested to express themselves on online interaction which produces a huge amount of data. The user generated content may contain user's emotions, opinions, daily events and specially their intent or motive behind their communication. Intention identification/mining of user's reviews, that is whether a user review contains intent or not, from social media network, is an emerging area and is in great demand in various fields like online advertising, improving customer services and decision making. Until now, a lot of work has been performed by researchers on user intention identification using machine learning approaches. However, it is demanded to focus on deep neural network methods. In this research work, we have conducted experimentation on intention dataset using a deep learning method namely CNN+BILSTM. The results exhibit that the proposed model efficiently performed identification of intention sentences in user generated text with a 90% accuracy.