Application of Big Data Analysis Based on Artificial Intelligence Algorithms in the Diagnosis and Intervention of Foreign Language Reading Disorders

With the popularization of foreign language education worldwide, foreign language reading disabilities have become an important bottleneck restricting students’ language skills improvement. Traditional diagnostic methods are based on manual evaluation, which has problems such as low efficiency, strong subjectivity, and insufficient targeted intervention plans. This article relies on artificial intelligence algorithms and big data analysis technology to construct an intelligent system for diagnosing and intervening in foreign language reading disorders. Firstly, multidimensional data such as students’ reading behavior, physiological responses, and language tests are obtained through the big data collection module. Then, machine learning algorithms are used to construct diagnostic models that accurately determine the type and severity of the disease. Finally, based on the diagnostic results, personalized intervention plans are generated and dynamically optimized, and the diagnostic accuracy and intervention effectiveness of the system are verified through experiments. The experimental results show that the accuracy of the AI big data analysis model in the three core indicators and comprehensive diagnosis is significantly higher than that of traditional manual diagnosis methods, with an average improvement of 19.2%. Among them, the improvement in word estimation speed recognition index was the largest (20.5%), providing an effective technical approach for accurate and intelligent diagnosis and treatment of foreign language reading disorders.