The operating cost of the port can be greatly reduced and economic benefits can be greatly improved by the automatic ship loading system， which is an important part of the smart port construction. Hatch recognition is the primary link in the automatic ship loading task， and its success rate and recognition accuracy are important guarantees for the smooth progress of subsequent tasks. Collected ship point cloud data is often missing due to issues such as the number and angle of the port lidars. In addition， the geometric information of the hatch cannot be expressed accurately by the collected point cloud data because there is often a large amount of material accumulation near the hatch. The recognition success rate of the existing algorithm is significantly reduced due to the frequent problems in the actual ship loading operation of the port mentioned above， which has a negative impact on the automatic ship loading operation. Therefore， it is urgent to improve the success rate of hatch recognition in the case of material interference or incomplete hatch data in the ship point cloud. A hatch recognition algorithm of bulk cargo ship based on incomplete point cloud normal filtering and compensation was proposed， by analyzing the ship structural features and point cloud data collected during the automatic ship loading process. Experiments were carried out to verify that the recognition success rate and recognition accuracy are improved compared with Miao’s and Li’s hatch recognition algorithms. The experimental results show that the proposed algorithm can not only filter out the material noise in the hatch， but also compensate for the missing data， which can effectively improve the hatch recognition effect.

Concerning the problem of random perturbation of controller in synchronous control of neural network with mixed delays， a non-fragile controller based on event-triggered mechanism was proposed. Firstly， a random variable obeying Bernoulli distribution was used to describe the randomness of the existence of controller gain disturbance. Secondly， the event-triggered mechanism was introduced in the synchronous control process of the neural network. Next， a novel bilateral Lyapunov function was constructed to fully consider the system status information， while the functional derivatives were scaled by an improved integral inequality to obtain sufficient conditions for the exponential stability of the synchronization error system. Finally， a non-fragile controller was designed based on the decoupling technique. The effectiveness of the proposed controller was verified by simulation examples. Experimental results show that compared with the existing exponential attenuation coefficient under the same sampling period in the four-tank system， the exponential attenuation coefficient obtained by the proposed controller is improved by 0.16.

Aiming at the dependencies between items are difficult to be captured by the present session-based recommendation models from short sessions， with complex item interactions and dynamic user interest changes considered， a Session-based Recommendation of Enhanced Capsule Network （SR-ECN） model was proposed. First， session sequence data was processed by using the Graph Neural Network （GNN） to obtain embedded vector of each item. Then， the dynamic routing mechanism of the capsule network was used to aggregate high-level user preferences from the interaction history. In addition， a self-attention network was introduced by the proposed model to further consider potential information about users and items， thereby recommending more suitable items for users. Experimental results show that， on Yoochoose dataset， the proposed model is superior to all comparison models such as Session-based Recommendation with GNN （SR-GNN）， Target Attentive GNN （TAGNN）， and the proposed model improves 0.92 and 0.45 percentage points compared to the Lossless Edge-order preserving aggregation and Shortcut graph attention for Session-based Recommendation （LESSR） model in terms of recommendation recall and Mean Reciprocal Rank （MRR） respectively.

Aiming at the lack of consideration of the psychological behaviors of decision makers in software quality evaluation methods， a TOmada de Decisao Interativa e Multicritevio （TODIM） software quality evaluation method based on interval 2-tuple linguistic information was proposed. Firstly， interval 2-tuple linguistic information was used to characterize the evaluation information of experts for software quality. Secondly， the subjective and objective weights of software quality attributes were calculated by subjective weighting method and Technique for Order Preference by Similarity to Ideal Solution （TOPSIS） respectively. On this basis， the comprehensive weights of software quality attributes were obtained by combined weighting method. Thirdly， in order to better describe the psychological behaviors of experts in the process of software quality evaluation， TODIM was introduced into software quality evaluation. Finally， the method was used to evaluate the software quality of assistant dispatcher terminal in high-speed railway dispatching system. The result shows that the third assistant dispatcher terminal software provided by the railway software supplier has the highest dominance value and its quality is the best. The results of comparing this method with the regret theory and Preference Ranking Organization METHod for Enrichment Evaluations （PROMETHEE-II） show that the three methods are consistent in the selection of the best quality software， but the overall rankings of the three methods are somewhat different， indicating that the constructed method has strong superiority in describing the interaction between multiple criteria and the psychological behaviors of decision makers.

To improve the accuracy of watershed runoff volume prediction， and considering the lack of model transparency and physical interpretability of data-driven hydrological model， a new runoff forecast model named Graph Attention neTwork and Dual-stage Attention mechanism-based Long Short-Term Memory network （GAT-DALSTM） was proposed. Firstly， based on the hydrological data of watershed stations， graph neural network was introduced to extract the topology of watershed stations and generate the feature vectors. Secondly， according to the characteristics of hydrological time series data， a runoff forecast model based on dual-stage attention mechanism was established to predict the watershed runoff volume， and the reliability and transparency of the proposed model were verified by the model evaluation method based on attention coefficient heat map. On the Tunxi watershed dataset， the proposed model was compared with Graph Convolution Neural network （GCN） and Long Short-Term Memory network （LSTM） under each prediction step. Experimental results show that， the Nash-Sutcliffe efficiency coefficient of the proposed model is increased by 3.7% and 4.9% on average respectively， which verifies the accuracy of GAT-DALSTM runoff forecast model. By analyzing the heat map of attention coefficient from the perspectives of hydrology and application， the reliability and practicability of the proposed model were verified. The proposed model can provide technical support for improving the prediction accuracy and model transparency of watershed runoff volume.

Knowledge graph representation learning aims to map entities and relations into a low-dimensional dense vector space. Most existing related models pay more attention to learn the structural features of the triples while ignoring the semantic information features of the entity relationships within the triples and the entity description information features outside the triples， so that the abilities of knowledge expression of these models are poor. In response to the above problem， a knowledge representation learning method BAGAT （knowledge representation learning based on BERT model And Graph Attention Network） was proposed by fusing multi-source information. First， the entity target nodes and neighbor nodes of the triples were constructed by combining knowledge graph features， and Graph Attention Network （GAT） was used to aggregate the semantic information representation of the triple structure. Then， the Bidirectional Encoder Representations from Transformers （BERT） word vector model was used to perform the embedded representation of entity description information. Finally， the both representation methods were mapped to the same vector space for joint knowledge representation learning. Experimental results show that BAGAT has a large improvement compared to other models. Among the indicators Hits@1 and Hits@10 on the public dataset FB15K-237， compared with the translation model TransE （Translating Embeddings）， BAGAT is increased by 25.9 percentage points and 22.0 percentage points respectively， and compared with the graph neural network model KBGAT （Learning attention-based embeddings for relation prediction in knowledge graphs）， BAGAT is increased by 1.8 percentage points and 3.5 percentage points respectively， indicating that the multi-source information representation method incorporating entity description information and semantic information of the triple structure can obtain stronger representation learning capability.