Analysis and Study on Intelligent Tourism Route Planning Scheme Based on Weighted Mining Algorithm

<div>Relationship between ant colony number <svg height="6.1673pt" id="M114" style="vertical-align:-0.2063904pt" version="1.1" viewbox="-0.0498162 -5.96091 10.3951 6.1673" width="10.3951pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M766 88L752 113C719 83 690 64 681 64C674 64 672 74 679 103L724 292C758 436 724 448 701 448C680 448 666 442 639 429C594 407 514 350 441 252H439L447 289C476 423 450 448 419 448C398 448 379 441 355 427C307 400 234 344 162 249H160L180 324C203 409 197 448 170 448C144 448 82 413 23 349L35 321C57 343 96 374 108 374C115 374 117 371 111 341C87 227 57 112 24 -6L32 -12C53 -4 81 4 108 6C119 68 134 128 149 171C177 229 309 383 364 383C387 383 388 355 373 282C354 190 330 92 303 -6L309 -12C332 -4 356 3 386 6C396 63 411 122 424 171C458 236 590 383 642 383C658 383 664 369 652 315L603 91C587 20 593 -12 619 -12C642 -12 708 23 766 88Z"></path></g></svg> and average path length.</div>

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Figure 4

Figure 4: Analysis and Study on Intelligent Tourism Route Planning Scheme Based on Weighted Mining Algorithm 