Knowledge Recommendation Method for Information Security Courses Based on BiLSTM-MA-FSBD
XU Xiaofeng
ZHAO Wei
BAO Xianglin
LIU Tao
YAN Nan
Abstract：To address the problems of insufficient fusion of multi-source behaviors and weak targeted preference adaptation in knowledge recommendation for information security courses,a knowledge recommendation method,namely,bidirectional long short-term memory-multi-head attention-fusion of student multi-source behavior data(BiLSTM-MA-FSBD)was proposed.Firstly,an integrated feature system covering dynamic time series and static correlations was constructed where multi-source behavior data of students were integrated and core behavioral features were extracted.Secondly,a BiLSTM network was designed to encode the dependency relationships of behavior sequences,and a MA mechanism was utilized to adaptively assign weights to behaviors,thus achieving accurate inference of learning preferences.Finally,a three-level knowledge graph for information security was built to quantify the dependency relationships among knowledge points,and personalized recommendations were implemented by combining preference matching degrees.The results indicated that the recommendation precision of the BiLSTM-MA-FSBD method was improved by 26.2 percentage points in comparison with the collaborative filtering(CF)method.This method could effectively adapt to the teaching characteristics of information security courses and the personalized learning needs of students,and it provided a feasible technical solution for solving the problem of accurate knowledge recommendation.
Keywords：recommendation modelmulti-source dataBiLSTMMAknowledge graphinformation security
Publication Date：2026-03-20
Online Publishing Date：2026-03-26(First online date of this platform, not the publication date of the document)
Pages：6( 137-142 )