With the development of big data (BD) technology, tourism route planning of historical blocks relies on a large amount of real-time data. The existing research data sources are limited and difficult to integrate, which cannot meet the personalized needs of tourists. This paper combined BD and intelligent algorithms to realize personalized tourism route planning of historical blocks. By collecting tourists’ behavioral data, scenic spot spatial data and real-time traffic information, the paper built tourist portraits and used the neural collaborative filtering algorithm to make personalized scenic spot recommendations. It used genetic algorithms (GAs) to optimize routes, taking into account factors such as tourists’ interests, distances between scenic spots, and traffic conditions. With the help of the real-time data streaming platform Apache Kafka, the paper dynamically adjusted routes to deal with sudden traffic or crowded attractions, thereby improving the tourist experience. The experimental results analyze the consumption preferences and behavioral characteristics of different tourists. Tourist 1002 spent 500 yuan on shopping, and high-end shopping malls and food courts were recommended for him. Tourist ID 1005 preferred “snacks and coffee” in terms of dining, and showed no interest in souvenir consumption. This tourist preferred to stay in leisure places for a longer time rather than a compact travel route. The neural coordination filtering algorithm + GA performed well in terms of total travel time of 4.2 hours, total walking distance of 7.8 kilometers, and traffic congestion coefficient of 0.35, which was better than other algorithms, showing its significant advantages in digital tourism route planning in historical blocks. This method combines BD and intelligent algorithms to improve the tourist experience through personalized recommendations and route optimization, optimize the traffic management of scenic spots, flexibly respond to emergencies, promote the intelligent and refined management of historical district tourism, and provide innovative ideas for future tourism route planning.