For the traditional module optimization community partition algorithms can only use the structure information of network

and cannot use the rich content information

leading to low precision problem.A community partition algorithm that is combined with the content attribute and empowers the edge to fully optimize the topology of the network

called CCSRW (Classification with Content-Structure and Random Walk) is proposed.We use random walk theory to calculate the similarity relationship matrix between structure nodes and content nodes

and map structure nodes onto the content attribute space

finally divide the community partition problems into multidimensional unsupervised clustering problems.Comprehensive experimental analysis on the real data sets shows that compared to the traditional community partition algorithms

this algorithm can describe the network structure more accurately

improve the classification performance significantly

and solve the problem that is not sensitive to small community effectively

and it is more suitable for the large-scale complex information network community partition.