Personalised recommendation method for smart library literature based on user behaviour feature perception

To solve the problem that existing library literature recommendation methods cannot achieve diversity and personalisation, this study proposes a personalised recommendation method for smart library literature based on user behaviour feature perception technology. Firstly, based on network coding technology, the collection of user behaviour data for smart libraries is completed, and the collected data is reduced in dimensionality through information entropy to remove redundant features. Then, a user behaviour feature model is constructed through Bayesian networks to perceive and analyse user behaviour, obtain user behaviour features, and finally, based on the feature perception results, a collaborative filtering algorithm is used to complete personalised recommendation of literature materials in the smart library. The experimental results show that this method can fully utilise the behavioural characteristics of users, accurately understand their interests and needs, and provide more accurate literature recommendation results.