Aiming at the problems of redundant information and too high dimension in high-dimensional data， a Maximum Correlation maximum Difference feature selection algorithm （MCD） based on the maximum correlation of information quantity was proposed. Firstly， the correlation between Mutual Information （MI） measurement features and labels was used to sort and select features with the largest mutual information into feature subsets according to the relevant knowledge of information theory. Then， the information distance was introduced to measure the information redundancy and difference between the two features， and the evaluation criteria were designed to evaluate each feature， so that the correlation between the features and labels， and the difference between the features were the largest. Finally， the forward search strategy combined with the evaluation criteria was used to reduce the attributes and optimize the feature subset. Using 2 different classifiers， comparative experiments were carried out on 6 datasets with 5 classical algorithms such as mRMR （minimal-Redundancy-Maximal-Relevance criterion） and RReliefF， and the validity of MCD was verified by using the classification accuracy. Under the Support Vector Machine （SVM） classifier， the average classification accuracy increased by 5.67 - 23.80 percentage points， respectively； and under the K-Nearest Neighbor （KNN） classifier， the average classification accuracy increased by 2.69 - 25.18 percentage points， respectively. It can be seen that in the vast majority of cases， MCD can effectively remove redundant features and significantly improve classification accuracy.

Concerning the problem of insufficient image feature extraction and ignorance of single-modal internal relations and the interactions between single-modal and multi-modal， a text and image information based Multi-Modal Deep Fusion （MMDF） model was proposed. Firstly， the Bi-Gated Recurrent Unit （Bi-GRU） was used to extract the rich semantic features of the text， and the multi-branch Convolutional-Recurrent Neural Network （CNN-RNN） was used to extract the multi-level features of the image. Then the inter-modal and intra-modal attention mechanisms were established to capture the high-level interaction between the fields of language and vision， and the multi-modal joint representation was obtained. Finally， the original representation of each modal and the fused multi-modal joint representation were re-fused according to their attention weights to strengthen the role of the original information. Compared with the Multimodal Variational AutoEncoder （MVAE） model， the proposed model has the accuracy improved by 1.9 percentage points and 2.4 percentage points on the China Computer Federation （CCF） competition and the Weibo datasets respectively. Experimental results show that the proposed model can fully fuse multi-modal information and effectively improve the accuracy of false information detection.