Abstract: To address the issues of redundant wiring, susceptibility of analog signals to interference, and insufficient system integration density in conventional triboelectric sensing arrays for wearable applications, this paper proposes a wearable intelligent interactive system based on a flexible printed circuit (FPC)-integrated triboelectric array. Micro/nano conical structures are fabricated on the surface of a nylon friction layer using an anodized aluminum oxide template hot-pressing process to enhance the contact electrification efficiency of the device. Meanwhile, the sensing units, front-end signal conditioning circuitry, analog-to-digital conversion, and wireless transmission modules are all integrated onto the FPC, shortening the analog signal transmission paths and enabling near-sensor digitization of multi-channel signals, thereby reducing signal attenuation and interference risks during long-distance transmission. The system employs a one-dimensional convolutional neural network (1D-CNN) combined with a multilayer perceptron (MLP) model to extract and classify the temporal features of five-channel triboelectric signals, achieving recognition accuracies of 94.2% for object shapes and 90.0% for gesture commands. Based on this, a closed-loop human–machine interaction platform is constructed to enable real-time control of remote unmanned vehicles, providing a feasible solution for the design of highly integrated, self-powered flexible interactive systems.