Construction and Push of Financial Instructional Resource Bank Based on Rough Set Theory

A perfect resource bank for financial education that can foster exchange and charitable work must be established. The traditional recommendation algorithm is improved in this paper based on the rough set theory, avoiding issue where similarity calculation does not match actual judgement. When used field of clustering education, has concept an approximate boundary problems. It more accurately describe groups classification. model offline, a K-means user used; users are assigned to upper lower approximations K classes how similar they cluster centers, creating initial neighbor users. Find target user’s nearest from online, predict item’s score, offer suggestions it. evaluation’s findings indicate system achieve precision rate 94.35 percent. Additionally, compared conventional method, recommended recall higher at 95.94 This effectively provide high-quality resources teaching while overcoming drawbacks algorithms, increasing accuracy.