Identifying　road　freight　cargo　types　is　crucial　for　regional　economic　interaction　and　transportation　optimization.　Existing　methods　primarily　rely　on　manual　labeling　and　the　rule,　neither　of　which　can　achieve　automated　semantic　enhancement　of　large-scale　road　freight　trajectories.　Consequently,　this　study　proposes　a　semi-supervised　trajectory　semantic　enhancement　method　for　identifying　cargo　types　based　on　trajectory　feature　extraction　and　point-of-interest　(POI)　association.　The　raw　trajectories　are　segmented　and　enriched　with　the　closest　POIs.　The　sample　labeling　method　with　POI　semantic　enhancement　is　then　proposed　using　company　registration　information.　Finally,　the　spatiotemporal　and　sequential　features　of　labeled　freight　trips　are　extracted　to　build　a　self-training　semi-supervised　model　for　identifying　the　cargo　type　of　road　freight.　Experimental　studies　on　real　trajectory　data　demonstrate　superior　accuracy　and　robustness　compared　to　existing　methods,　with　accuracy　and　F1　values　reaching　81.4　and　0.77%,　respectively.　The　proposed　sample　labeling　method　improves　representativeness　and　universality,　increasing　accuracy　by　7.8-14.4%　and　F1　value　by　8.5-34.5%　compared　to　the　rule-based　method.　The　semi-supervised　model　improves　accuracy　by　8.9%　and　F1　value　by　29.1%　compared　to　the　supervised　model　when　only　10.0%　of　samples　were　labeled.　This　method　enables　automatic　and　full-sample　cargo　type　identification　in　real-world　large-scale　transportation　systems.