Generative text summary is an important branch of natural language processing. Aiming at the problems of insufficient use of semantic information, insufficient summary precision and the problem of semantics-loss in the current generated text summary method, an enhanced semantic model is proposed based on dual-encoder, which can provide richer semantic information for sequence-to-sequence architecture through dual-encoder. The enhanced attention architecture with dual-channel semantics is optimized, and the empirical distribution and Gain-Benefit gate are built for decoding. In addition, the position embedding and word embedding are merged into the word embedding technology, and the TF-IDF(term frequency-inverse document frequency), part of speech, key score are added to word's feature. Meanwhile, the optimal dimension of word embedding is optimized. This paper aims to optimize the traditional sequence mapping and word feature representation, enhance the model's semantic understanding, and improve the quality of the summary. The LCSTS and SOGOU datasets are used to validate proposed method. The experimental results show that the proposed method can improve the performance of the ROUGE evaluation system by 10-13 percentage points compared with other listed algorithms. We can observe that the semantic understanding of the text summaries is more accurate and the generation effect is better, which has a better application prospect.

Based on the research of business continuity and information security of the Internet of Things (IoT), a key business node identification model for the Internet of Things security is proposed. First, the business nodes are obtained based on the business process, and the importance decision matrix of business nodes is constructed by quantifying the evaluation attributes of nodes. Second, the attribute weights are improved by the analytic hierarchy process (AHP) and entropy weighting method from subjective and objective dimensions to form the combination weight decision matrix, and the analytic hierarchy process and entropy weighting VIKOR (AE-VIKOR) method are used to calculate the business node importance coefficient to identify the key nodes. Finally, according to the NSL-KDD dataset, the network security events of IoT network intrusion detection based on machine learning are monitored purposefully, and after the information security event occurs in the smart mobile phone, which impacts through IoT on the business system, the impact of the key business node on business continuity is analyzed, and the business continuity risk value is calculated to evaluate the business risk to prove the effectiveness of the model. The experimental results of the civil aviation departure business show that the AE-VIKOR method can effectively identify key business node, and the impact of the key business node on business continuity is analyzed, which further proves the efficiency and accuracy of the model in identifying the key business node.

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