In today’s world, it is verifiable that social media plays an essential job in affecting our way of life, our economy and our general perspective of the world. Social media is a new forum that conveys individuals to exchange idea, connect with, relate to, and mobilize for a cause, seek advice, and offer guidance. Most research on social network mining centers around finding the knowledge behind the information for enhancing people life. While multimedia social networks (MSNs) apparently grow their users' ability in expanding social contacts, they may really diminish the face-to-face interpersonal connections in reality. In this way, the association between users and MSN are becoming more progressively complete and convoluted. This proposed system principally expanded and enhanced the circumstance investigation system for the particular social area and further proposed a novel algorithm for users intention serialization analysis dependent on exemplary Generalized Sequential Pattern (GSP). We utilized the enormous volume of user behavior records to investigate the continuous sequence mode that is important to predict user intention. The experiments chose two general sorts of goals: playing and sharing of interactive media, which are the most widely recognized in MSN, based on the intention serialization algorithm under various least threshold limit (Min Support).