The complexity of information data makes strengthening information evaluation and screening an important research content. Traditional association analysis algorithms are difficult to perform strong analysis on association rules, and they do not sufficiently grasp data feature information. Therefore, based on the original association classification algorithm, multiple learning of training sets and setting of interest threshold are studied to improve the correlation between category labels and reduce the interference of redundant information. Subsequently, multiple tag feature selection algorithms and frequent pattern trees are introduced to measure data information using tag importance as a metric, improving information processing efficiency, and implementing algorithm processing through local connection and pruning operations. The information evaluation results of the improved algorithm proposed in the study show that the training error of the improved association classification (AC) algorithm is 2.36%, and the maximum training error difference and time consumption range between the improved AC algorithm and other algorithms reach 31.28% and 14.28%, greatly improving the operational efficiency and classification accuracy of the algorithm. Simultaneously improve the average classification error and Recall@ K Increased to 10.37% and above 5%, effectively achieving information evaluation accuracy. The information evaluation algorithm can effectively provide practical tools and method reference value for data mining and related management work.