Artificial intelligence for the intelligent Internet of Everything (I-IoE) establishes a four-dimensional interconnection among people, data, processes, and things, providing possibilities for the next generation of industrial revolution. Against the ultra-high power consumption brought by massive connectivity, passive backscatter communication has emerged as a promising paradigm for the I-IoE. However, even with a well-designed communication module, integrating sensing components significantly multiplies the overall system complexity and cost in the context of the {0,1} modulation paradigm. In this paper, we introduce a [0,1] modulated backscatter architecture that achieves the seamless integration of passive sensing and communication. Unlike the conventional backscatter of reading, encoding, and reflecting, the proposed [0,1] modulated backscatter directly converts environmental physical quantities into continuous frequency-modulated square waves. Building on this concept, we propose an intelligent detection method based on graph dimensionality reduction. This method achieves low computational complexity at the receiver by leveraging a pre-measured dataset. We then present a hardware communication system that performs data collection, transmission, and demodulation, providing experimental validation for the proposed architecture. Furthermore, simulation results verify the feasibility of a continuous frequency division multiple-access method for large-scale tags. The experimental results demonstrate the superior performance of the proposed scheme. This work provides a potential breakthrough for achieving ultra-low-power integrated sensing and communication in sixth-generation wireless communication networks.