Design of Adaptive Integration Algorithm of Network Learning Resources for Labor Education

In the field of labor education, online learning resources can provide rich educational content and learning opportunities, helping learners acquire more knowledge and skills. However, due to the large and diverse number of learning resources on the internet, learners often face difficulties in choosing and information overload. To improve the retrieval speed and utilization rate of online learning resources in labor education, an adaptive integration algorithm for online learning resources in labor education is proposed. Firstly, under the constraints of the integration principle, set the triggering conditions for the integration of online learning resources and collect labor education online learning resources. Then, Kalman filter technology is used to filter the initial network learning resources and extract the content features of network learning resources. Finally, the similarity and integration weight between online learning resources are calculated in an adaptive form to achieve adaptive integration of labor education online learning resources. According to the experimental results, compared with traditional integration algorithms, the optimized design algorithm reduces the loss and error rates by 0.12 GB and 7.35%, respectively. At the same time, the retrieval speed and utilization rate of learning resources are significantly improved.