A text classification method in Natural Language Processing （NLP） was introduced into the field of criminal psychology to scientifically and intelligently grade the violent tendencies of prisoners. A Criminal semantic Convolutional Hierarchical Attention Network （CCHA-Net） based on the joint modeling of two channels of improved HAN （Hierarchy Attention Network） and TextCNN （Text Convolutional Neural Network） was proposed to complete the violent criminal temperament grade by separately mining the semantic information of crime facts and basic information of prisoners. Firstly， Focal Loss was used to simultaneously replace the Cross-Entropy function in both channels to optimize the sample size imbalance problem. Secondly， in the two-channel input layer， positional encoding was simultaneously introduced to improve the perception of positional information. The HAN channel was improved by using max-pooling to construct salient vectors. Finally， global average pooling was used to replace the fully connected method in all output layers to avoid overfitting. Experimental results show that compared with 17 related baseline models such as AC-BiLSTM （Attention-based Bidirectional Long Short-Term Memory with Convolution layer） and Support Vector Machine （SVM）， the indicators of CCHA-Net reach the best， the micro-average F1 （Micro_F1） is 99.57%， and the Area Under the Curve （AUC） under the macro-average and the micro-average are 99.45% and 99.89%， respectively， which are 4.08， 5.59 and 0.74 percentage points higher than those of the suboptimal AC-BiLSTM. It can be verified that the violent criminal temperament grade task can be effectively performed by CCHA-Net.

In order to improve the security of watermarking algorithm, a robust blind watermarking based on two forms of key was proposed. Firstly, the watermark was encrypted by a key, then matrix Q was consisted of the first singular value of each block carrier and block Discrete Wavelete Transform (DWT) again to acquire four subbands, the k-th watermark bit was chosen to be embedded in the k-th block's low-frequency, horizontal, vertical and high-frequency subbands of matrix Q by Markov Chain Monte Carlo (MCMC) sampling of four subbands and record the current key of embedded subband. It not only made watermark bit randomization, but also improved the safety of the watermark algorithm. The experimental results show that the proposed watermarking has strong robustness against conventional attacks under the condition of satisfying invisibility, meanwhile, it enhances the security of the watermark algorithm, which is embedded with a different key by MCMC sampling in the watermark embedding process.

To solve the problem of location verification caused by collusion attack in Vehicular Ad Hoc NETworks (VANET), a multi-round vote location verification based on weight and difference was proposed. In the mechanism, a static frame was introduced and the Beacon messages format was redesigned to alleviate the time delay of location verification. By setting malicious vehicles filtering process, the position of the specific region was voted by the neighbors with different degrees of trust, which could obtain credible position verification. The experimental results illustrate that in the case of collusion attack, the scheme achieves a higher accuracy of 93.4% compared to Minimum Mean Square Estimation (MMSE) based location verification mechanism.