With the rapid development of the Internet, network media, as a new form of information dissemination, has penetrated into people’s daily life. In recent years, with the rapid transformation of Chinese social structure and the rise of self-media platforms, various social contradictions have been highlighted in the form of online public opinion. Especially on online multimedia platforms, the spread of online public opinion is more rapid, which can easily lead to social hotspots. In order to effectively supervise the public opinion information on the Internet, it is necessary to identify the target of the information on the multimedia platform and effectively screen the information, so as to control the network public opinion in the development stage. Aiming at the above problems, we propose a multitarget retrieval method based on a convolutional neural network, which uses multitarget detection algorithm to locate multitarget regions and extract regional features and uses cosine distance as a similarity measure for multitarget recognition. In view of the slow feature extraction speed of VGG model, a lightweight mobile network model is proposed to replace the original VGG model on the mobile phone to reduce the retrieval time and realize the recognition of specific targets on the multimedia platform, and it is applied to the verification of image recognition on the multimedia platform. The results show that the algorithm proposed in this paper has great advantages in multitarget recognition tasks.

Background: Stomach cancer, also known as gastric adenocarcinoma, remains the most common and deadly cancer worldwide. Its early diagnosis and prevention are effective to improve the 5-year survival rate of the patients. Therefore, it is important to discover specific biomarkers for early diagnosis and drug treatment. This study investigates the potential key genes and signaling pathways involved in gastric cancer. Methods: The gene expression profiles, GSE63089, GSE33335, and GSE79973, were retrieved for the identification of differentially expressed genes (DEGs) within a total of 80 gastric cancer samples and 80 normal samples. A total of 1423 up- and 1155 downregulated genes were screened for overlapping DEGs visualized via Venn diagrams along with 58 upregulated and 43 downregulated genes. These overlapping DEGs were evaluated with gene ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein-protein interaction (PPI) network analysis. Using DAVID software, we identified several genes enriched in both GO and KEGG analyses. PPI analysis was performed with STRING software, and 3 submodules were obtained with Cytoscape software. Then, we used Cytohubba with 12 classification methods to select candidate hub genes. The group 1 genes enriched in GO and KEGG pathway intersected with group 2 genes, which were approved by nine algorithms, and group 3 genes clustered in three submodules. 9 hub genes were intersected from group 1/2/3 genes and the prognostic values were estimated through GEPIA. We found that the LUM and COL1A1 expression levels and survival outcomes displayed a favorable prognostic value (P-value = 0.013 for LUM and P-value =0.042 for COL1A1). Results: Finally, 5 machine learning methods were employed for the validation of two hub genes (COL1A1, LUM) to distinguish between the cancer samples and non-cancer samples. The accuracy of XGBoost was estimated to be 0.9375, and the precision and specificity as 1.000. The highest recalls of LR and MLP were 1.0000, and the AUC was 1.0000. In the test set GSE65801, the accuracy of all models was greater than 80%, and the XGBoost model obtained the highest prediction accuracy of 0.8906. The precision of 0.9301 and the specificity of 0.9375 were obtained. The highest recall of MLP was 0.8750 and AUC was 0.9082. The correlation of prognostic indicators with the tumor-infiltrating immune cell levels was analyzed using TIMER. Conclusion: The identified hub genes explored in this study would enhance the understanding of the molecular mechanism of gastric cancer and may be regarded as a potential therapeutic target as assessed by integrating bioinformatics and machine learning methods