Nowadays, document retrieval was an important way of academic exchange and achieving new knowledge. Choosing corresponding category of database and matching the input key words was the traditional document retrieval method. Using the method, a mass of documents would be got and it was hard for users to find the most relevant document. The paper put forward text quantification method. That was mining the features of each element in some document, which including word concept, weight value for position function, improved weights characteristic value, text distribution function weights value and text element length. Then the word’ contributions to this document would be got from the combination of five elements characteristics. Every document in database was stored digitally by the contribution of elements. And a subject mapping scheme was designed in the paper, which the similarity calculation method based on contribution and association rule was firstly designed, according to the method, the documents in the database would be conducted text clustering, and then feature extraction method was used to find class subject. When searching some document, the description which users input would be quantified and mapped to some class automatically by subject mapping, then the document sequences would be retrieved by computing the similarity between the description and the other documents’ features in the class. Experiment shows that the scheme has many merits such as intelligence, accuracy as well as improving retrieval speed.
CFNTT: Scalable Radix-2/4 NTT Multiplication Architecture with an Efficient Conflict-free Memory Mapping Scheme
