# Research on Intelligent Perception Algorithm for Sensitive Information
###### Abstract  
In the big data era, a tremendous volume of electronic documents is transmitted via the network, many of which include sensitive information about the country and businesses. There is a pressing need to be able to perform intelligent sensing of sensitive information on these documents in order to be able to discover and guarantee the security of sensitive information in this enormous volume of documents. Although the low effectiveness of manual detection is resolved by the current method of handling sensitive information, there are still downsides, such as poor processing effects and slow speed. This study creatively proposes the Text Sensitive Information Intelligent Perception algorithm (TSIIP), which detects sensitive words at the word level and sensitive statements at the statement level to obtain the final assessment score of the text. We experimentally compare this algorithm with other methods on an existing dataset of sensitive Chinese information. We use the metrics measuring the accuracy of the binary classification model, where the F1 score reaches 0.938 (+0.6%), and the F2 score reaches 0.946 (+1%), and the experimental results fully demonstrate the superiority of this algorithm. sensitive information; intelligent perception; intelligent algorithm +
Footnote †: journal: Medical  
frequency on the basis of one-hot. The bag-of-words approach partially solves the problem of vector sparsity compared to the one-hot method, but it does not take into account word order as well as relationships between words and is unable to capture contextual information. The N-gram method [3], which is based on the bag-of-words model, takes all neighboring N words into account as a whole, assuming that each word is related to the n-1 words that came before it or that the n-th word can be inferred from the preceding n-1 words. It is common to use the bi-gram method with two words as a whole and the tri-gram method with three words as a whole. The probability of the text as a whole is the product of the probabilities of the word occurrences. Although this approach includes contextual information and takes word order inside phrases into account, it results in more severe data sparsity when the vocabulary is large. The term frequency-inverse document frequency (TF-IDF) method [4] determines whether a word is unique to this document by taking into account its frequency in this document and its frequency in other documents in the corpus. This method incorporates lexical features and textual information into the word vector. However, it still does not reflect word position information and contextual information, and the semantic relationships that exist between words cannot be characterized by textual semantic relationships through frequency alone. The second method is the language model based on neural networks, which has become very popular in recent years. Inspired by the CNN neural network structure [5], [PERSON] et al. [6] used the first n-1 words to predict the probability of occurrence of the n-th word using contextual modeling in order to produce a word vector representation of each word. However, manual extraction results in a restricted ability to represent complicated functions and perform challenging classification problems, as well as an incomplete and inaccurate representation of emotion. [PERSON] et al. proposed the Word2 vec model [7], which contains the CBOW and Skip-gram model approaches to obtain word vectors by computing the association of words with their contexts. The CBOW model predicts the middle target word given the surrounding words, whereas the Skip-gram model predicts the surrounding words given the middle words. These two methods' computing complexity is too great when the vocabulary is broad, though. [PERSON] et al. proposed the Glove (global vector for word representations) model [8] to solve the issue that the Word2 vec model does not take global aspects into account. This model applies global features while also taking into account local contextual data.