In unmanned farms, machinery transfer between fields and access to field entrances are essential prerequisites for autonomous field operations, and both require support from an accurately structured farm-road network. However, existing road data are typically maintained as vector layers and lack the topological relationships and geometric attributes needed for transfer-route and field-entrance planning. This study proposes a method for constructing farm-road topological maps from road and field vector data. The method converts road polygons into a node–edge graph containing centerline geometry, estimates road-segment widths to support the safe passage of agricultural machinery, and establishes bidirectional road–field associations based on field-access nodes. Experiments in three farm areas show that the proposed method achieves a mean symmetric centerline error of 0.094 m; width-estimation mean absolute error (MAE) and root mean square error (RMSE) of 0.032 m and 0.060 m, respectively; and a 100% success rate in 50 random path-planning tasks. The farm-road topological map constructed by this method provides spatial infrastructure for agricultural-machinery path planning and operation scheduling in unmanned farms.