In order to improve the security of data center optical interconnection network components and software in artificial intelligence environment ,it is necessary to build an optimized resource allocation model and propose a re- source allocation method for data center optical interconnection network based on deep neural network. The resource scheduling model of the data center optical interconnection network is constructed by using the joint optimization meth- od of user association and power spectrum allocation. The integration and clustering of different types of resources are realized by combining the QoS resource allocation of service requests for network resource granularity ,and the spatial ,temporal ,spectral and other multidimensional grid abstract model parameters of the data center optical interconnection network resources are extracted ,The deep neural network learning method is used to realize the multi-resource granu- larity fusion and convergence optimization control in the process of network resource allocation ,establish the channel model for allocating the network resources of the data center optical interconnection between users ,and realize the opti- mal allocation and balanced allocation of network resources through the transmission link balanced configuration scheme. The simulation results show that the resource allocation transmission bit rate of this method is 18 bit/s the delay is small ,the resource allocation blocking rate is low ,which is 0. 05% ,and the resource holding rate is high ,which can always be maintained at 100% ,indicating that this method has a strong ability of resource balanced alloca-tion.