The current building assembly path planning often does not consider the impact of the movement of materials and equipment, which often leads to path conflicts or congestion. This paper uses dynamic environmental perception and real-time adjustment algorithms to cope with environmental changes in construction. Sensors such as laser scanners and cameras are used to comprehensively collect on-site data, and these data are processed through edge computing technology to ensure accuracy and timeliness. Using the A* algorithm, combined with Euclidean distance as a heuristic guide, the optimal initial path is quickly generated and determined. In order to solve the problem of multi-objective optimization, GA (genetic algorithm) is used to further optimize the path, taking into account factors such as path length, equipment load, and operating safety distance. The paper integrates the A* algorithm and GA to realize real-time path adjustment feedback and flexibly respond to changes in the construction site. Experiments show that the average path of the method combining A* and GA in all tests is 324.93 m, and the adjustment response time is 1.23 s, which is far better than the traditional algorithm of 470.55 m and 2.43 s. This shows that the method in this paper can reduce paths and conflicts, respond faster, improve the quality, efficiency and safety of building assembly path planning, and show its advantages and wide application potential.