Focusing on the issues of low quality of image caption, insufficient utilization of image features and single-level structure of recurrent neural network in image caption generation, an image caption generation algorithm based on multi-attention and multi-scale feature fusion was proposed. The pre-trained target detection network was used to extract the features of the image from the convolutional neural network, which were input into the multi-attention structures at different layers. Each attention part with features of different levels was related to the multi-level recurrent neural networks sequentially, constructing a multi-level image caption generation network model. By introducing residual connections in the recurrent networks, the network complexity was reduced and the network degradation caused by deepening network was avoided. In MSCOCO datasets, the BLEU-1 and CIDEr scores of the proposed algorithm can achieve 0.804 and 1.167, which is obviously superior to top-down image caption generation algorithm based on single attention structure. Both artificial observation and comparison results velidate that the image caption generated by the proposed algorithm can show better details.

In order to accurately detect the community structure of complex networks, a community detection algorithm based on signal adaptive transmission was proposed. First, the signal was adaptively passed on complex networks,thereby getting the vector affecting on the entire network of each node, then the topological structure of each node was translated into geometrical relationships of algebra vector space. Thus, according to the nature of the clustering, the community structure of the network was detected. In order to get the feasible spatial vectors, the optimum transfer number was determined, which reduced the searching space, and effectively strengthened the search capability of community detection.The proposed algorithm was tested on computer-generated network, Zachary network and American college football network. Compared with Girvan-Newman (GN) algorithm, spectral clustering algorithm,extremal optimization algorithm and signal transmission algorithm, the results show that the accuracy and precision of the proposed community division algorithm is feasible and effective.