With the widespread application of software across various domains， software vulnerabilities have shown a continuous upward trend， so that deep learning-based methods for vulnerability detection have gained wide application. However， the existing graph representation learning methods often neglect the influence of edges in the graph on vulnerability detection， and have the representation of edge weights too coarse. To address this issue， a software vulnerability detection method based on edge weight — EWVD （Edge Weight for Vulnerability Detection） was proposed. Firstly， comments， custom variable names， and function names in the source code were cleaned and represented abstractly. Secondly， Sent2Vec was selected to perform embedding representation after comparative analysis. Thirdly， edge weights were calculated comprehensively using three metrics： connection structure， the importance of neighboring nodes， and Jaccard similarity， so as to identify the information transmission capability between nodes. Finally， by leveraging edge weights， perception capability of the model was enhanced for potential relationships between vulnerable statements， thereby determining the importance of edges in the graph. Compared with the best-performing baseline method VulCNN among seven vulnerability detection baseline methods， EWVD achieves an increase of 1.06 percentage points in Accuracy and a decrease of 1.11 percentage points in False Positive Rate （FPR）. It can be seen that EWVD refines the representation of edge weights and improves the overall performance of vulnerability detection.

It is of great social value and times background significance to filter and recognize spam messages. Traditional artificially designed feature selection methods may lead to data sparseness, insufficient co-occurrence of feature information and difficulty in feature extraction. To solve above problems, a spam messages recognizing method based on word embedding and convolutional neural network was proposed. Firstly, word2vec's skip-gram model was used to train the word embedding of each word in the short message dataset according to the Wiki Chinese corpus, and the two-dimensional feature matrix representing short message was composed of word embedding of each word in a short message. Then, the feature matrix was used as the input to the convolutional neural network. The multi-scale short message features were extracted by using different scale convolution kernels of the convolution layer, and the 1-max pooling strategy was used to obtain the local optimal features. Finally, the fusion feature vector, composed of the local optimal features, was put into the softmax classifier to get the classification results. Experiments were performed on 100000 short messages. The experimental results show that the recognition accuracy based on the convolutional neural network model can reach 99.5%, which is 2.4% to 5.1% higher than that of the traditional machine learning models with the same feature extraction method, and the recognition accuracy of each model maintains above 94%.