Loess　landforms　in　the　Loess　Plateau　are　typical　landforms　in　arid　and　semiarid　areas　and　have　a　significant　impact　on　the　environment　and　soil　erosion.　Quantitative　analyses　on　loess　landform　have　been　employed　from　various　perspectives.　Peak　intervisibility　can　provide　the　potential　topographic　information　implied　in　the　visual　connectivity　of　peaks,　however,　its　application　in　loess　landform　analysis　remains　unexplored.　In　this　study,　the　interwoven　sightlines　among　peaks,　representing　peak　intervisibility,　were　extracted　from　the　digital　elevation　model　and　simulated　into　a　peak　intervisibility　network　(PIN).　Nine　indices　were　proposed　to　quantify　the　PIN.　Through　a　case　study　in　Northern　Shaanxi,　China,　three　tasks　were　conducted,　including,　landform　interpretation,　spatial　pattern　mining,　and　landform　classification.　The　main　findings　are　as　follows:　(1)　PIN　responds　to　terrain　morphology　and　is　beneficial　for　loess　landform　interpretation.　(2)　The　spatial　patterns　of　PIN　indices　are　heterogeneous　and　strongly　coupled　with　the　terrain　morphologies,　showing　anisotropy　and　autocorrelation　in　spatial　variations.　(3)　Using　the　light　gradient　boost　machine　classifier,　the　PIN　index-based　classification　reaches　a　mean　accuracy　of　86.09%,　an　overall　accuracy　of　86%　and　a　kappa　coefficient　of　0.84.　These　findings　shed　light　on　the　applicability　of　PIN　in　loess　landform　analysis.　Peak　intervisibility　not　only　enriches　the　theories　and　methodologies　of　relation-based　digital　terrain　analysis,　but　also　enhances　our　comprehension　of　loess　landform　genesis,　morphology,　distribution,　and　evolution.