Objectives  The load balancing of map information under multiple constraints is one of the difficulties in cartographic generalization. In small and medium-scale maps, it is necessary to comprehensively consider their administrative levels, topologies and metric relationships for the scale conversion of townships and village residential point features to make the map information load reasonable at a certain scale.

  Methods  This paper proposes a method for selecting residential point features based on optimal information entropy constraints. Under the constraints of optimal information entropy, the metric relationship constraints are adjusted, the semantic relationships are prioritized, the residential point features with higher administrative levels are reserved, and for the residential point features with lower administrative levels, if they are not the endpoints of the road and do not satisfy the metric relationship constraint, then the points are deleted, and the process is iterated until the optimal information entropy constraint is satisfied.

  Results  Experiments with 1∶250 000 residential point data have realized the selection of residential location elements that maintain topological consistency, level priority, and metric rationality. The load balancing and readability of the map are effectively maintained, meanwhile, the amount of effective information of the map is maximized based on the algorithm.

  Conclusions  The optimal information entropy constraint is adopted for the selection of residential points, which can retain the density characteristics of the spatial distribution of the residential point group as a whole, and achieve the load balancing of map information in effect.