Semi-supervised　learning　(SSL)　focuses　on　the　way　to　improve　learning　efficiency　through　the　use　of　labeled　and　unlabeled　samples　concurrently.　However,　recent　research　indicates　that　the　classification　performance　might　be　deteriorated　by　the　unlabeled　samples.　Here,　we　proposed　a　novel　graph-based　semi-supervised　algorithm　combined　with　particle　cooperation　and　competition,　which　can　improve　the　model　performance　effectively　by　using　unlabeled　samples.　First,　for　the　purpose　of　reducing　the　generation　of　label　noise,　we　used　an　efficient　constrained　graph　construction　approach　to　calculate　the　affinity　matrix,　which　is　capable　of　constructing　a　highly　correlated　similarity　relationship　between　the　graph　and　the　samples.　Then,　we　introduced　a　particle　competition　and　cooperation　mechanism　into　label　propagation,　which　could　detect　and　re-label　misclassified　samples　dynamically,　thus　stopping　the　propagation　of　wrong　labels　and　allowing　the　overall　model　to　obtain　better　classification　performance　by　using　predicted　labeled　samples.　Finally,　we　applied　the　proposed　model　into　hyperspectral　image　classification.　The　experiments　used　three　real　hyperspectral　datasets　to　verify　and　evaluate　the　performance　of　our　proposal.　From　the　obtained　results　on　three　public　datasets,　our　proposal　shows　great　hyperspectral　image　classification　performance　when　compared　to　traditional　graph-based　SSL　algorithms.