Handling complex data in uncertain environments has been concerned for a long time. In order to solve the problems of dealing with multidimensional data in fuzzy linguistic environments and mining rules contained between attributes described by linguistic values in different domains， an association rule extraction method based on triadic fuzzy linguistic formal context was proposed. Firstly， a triadic fuzzy linguistic formal context was developed by combining a linguistic term set with triadic concept analysis theory. Subsequently， triadic fuzzy linguistic concepts were defined on the basis of derivation operators， and an incremental construction idea was applied to develop a knowledge discovery algorithm based on triadic fuzzy linguistic formal context， thereby acquiring conceptual knowledge with semantic information under fuzzy triadic relations， as the result， the relationships between concept knowledge were depicted through constructing a triadic fuzzy linguistic diagram. Finally， an association rule extraction method based on triadic fuzzy linguistic concepts was introduced to explore correlations between attributes， resulting in semantic rules with conditional constraints. Experimental results on real datasets of different domains show that the proposed method handles multidimensional data effectively in fuzzy linguistic environments， acquires conceptual knowledge with semantic information， and mines semantic rules with high credibility.