Graphlet Degree Vector （GDV） is an important method for studying biological networks， and can reveal the correlation between nodes in biological networks and their local network structures. However， with the increasing number of automorphic orbits that need to be researched and the expanding biological network scale， the time complexity of the GDV method will increase exponentially. To resolve this problem， based on the existing serial GDV method， the parallelization of GDV method based on Message Passing Interface （MPI） was realized. Besides， the GDV method was improved and the parallel optimization of the optimized method was realized. The calculation process was optimized to solve the problem of double counting when searching for automorphic orbits of different nodes by the improved method， at the same time， the tasks were allocated reasonably combining with the load balancing strategy. Experimental results of simulated network data and real biological network data indicate that parallel GDV method and the improved parallel GDV method both obtain better parallel performance， they can be widely applied to different types of networks with different scales， and have good scalability. As a result， they can effectively maintain the high efficiency of searching for automorphic orbits in the network.