Stock market is an essential element of financial market, therefore, the study on volatility of stock market plays a significant role in taking effective control of financial market risks and improving returns on investment. For this reason, it has attracted widespread attention from both academic circle and related industries. However, there are multiple influencing factors for stock market. Facing the multi-source and heterogeneous information in stock market, it is challenging to find how to mine and fuse multi-source and heterogeneous data of stock market efficiently. To fully explain the influence of different information and information interaction on the price changes in stock market, a graph neural network based on multi-attention mechanism was proposed to predict price fluctuation in stock market. First of all, the relationship dimension was introduced to construct heterogeneous subgraphs for the transaction data and news text of stock market, and multi-attention mechanism was adopted for fusion of the graph data. Then, the graph neural network Gated Recurrent Unit (GRU) was applied to perform graph classification. On this basis, prediction was made for the volatility of three important indexes: Shanghai Composite Index, Shanghai and Shenzhen 300 Index, Shenzhen Component Index. Experimental results show that from the perspective of heterogeneous information characteristics, compared with the transaction data of stock market, the news information of stock market has the lagged influence on stock volatility; from the perspective of heterogeneous information fusion, compared with algorithms such as Support Vector Machine (SVM), Random Forest (RF) and Multiple Kernel k-Means (MKKM) clustering, the proposed method has the prediction accuracy improved by 17.88 percentage points, 30.00 percentage points and 38.00 percentage points respectively; at the same time, the quantitative investment simulation was performed according to the model trading strategy.
In order to solve the problem of feature information loss caused by the introduction of a large number of pooling layers in traditional convolutional neural networks, based on the feature of Capsule Network (CapsNet)——using vector neurons to save feature space information, a network model 3DSPNCapsNet (3D Small Pooling No dense Capsule Network) was proposed for recognizing 3D models. Using the new network structure, more representative features were extracted while the model complexity was reduced. And based on Dynamic Routing (DR) algorithm, Dynamic Routing-based algorithm with Length information (DRL) algorithm was proposed to optimize the iterative calculation process of capsule weights. Experimental results on ModelNet10 show that compared with 3DCapsNet (3D Capsule Network) and VoxNet, the proposed network achieves better recognition results, and has the average recognition accuracy on the original test set reached 95%. At the same time, the recognition ability of the network for the rotation 3D models was verified. After the rotation training set is appropriately extended, the average recognition rate of the proposed network for rotation models of different angles reaches 81%. The experimental results show that 3DSPNCapsNet has a good ability to recognize 3D models and their rotations.
Aiming at the problem of low network bandwidth utilization rate of the original data recovery mechanism in Information Centric Networking (ICN), a Network Coding based Real-time Data Retransmission (NC-RDR) algorithm was proposed. Firstly, the lost data packets in the network were counted according to the real-time status of the network. Then, network coding was combined into ICN, and the statistical lost data packets were combinatorially coded. Finally, the encoded data packets were retransmitted to the receiver. The simulation results show that compared with NC-MDR (Network Coding based Multicast Data Recovery) algorithm, in the transmission bandwidth aspect, the average number of transmissions was reduced by about 30%. In ICN, the proposed algorithm can effectively reduce the number of data re-transmissions, improveing network transmission efficiency.